Examining the validity of ERAInterim for tropical hurricanes. over the course of time TIM BIJSTERBOSCH

Transcription

1 Raffaele Crapolicchi o Digitally signed by Raffaele Crapolicchio DN: cn=raffaele Crapolicchio, o=serco spa, ou, =raffaele.crapolicchio@ser co.com, c=it Date: :36:52 +01'00' Examining the validity of ERAInterim for tropical hurricanes over the course of time TIM BIJSTERBOSCH 1 February 2016

2 Examining the validity of ERA- Interim for tropical hurricanes over the course of time Author: Tim Bijsterbosch Supervisor: Ad Stoffelen Mentor: Jos de Kloe 1 September 2015 to 1 February 2016 R&D satellite observations KNMI de Bilt, The Netherlands i

3 Abbreviations ASCAT Advanced Scatterometer ECMWF European Centre for Medium-Range Weather Forecasts ERA ECMWF re-analysis ERS-2 European Remote Sensing-Satellite (nr. 2) ESA European Space Agency EUMETSAT European Organisation for the Exploitation of Meteorological Satellites GMF Geophysical Model Function KNMI Koninklijk Nederlands Meteorologisch Instituut QuikSCAT Quick Scatterometer RSS Remote Sensing Systems SSMI Special Sensor Microwave Imager SSMIS Special Sensor Microwave Imager Sounder WVC Wind vector cell ii

4 Summery (EN) In this research the validity of ERA-Interim for tropical hurricanes over the course of time is examined. ERA-Interim is a reanalysis which uses one of the leading weather prediction models to reforecast the state of the atmosphere several times per day. This means that the only thing that changes the way ERA-Interim calculates these reforecasts are the input measurements used as initial state, which improve in quantity and quality with time. To examine if the validity of ERA-Interim for tropical hurricanes also improves with time the 10 m height wind product modelled by ERA-Interim will be compared to 10 m height wind product derived from satellite observations over a period of time. The satellite observations which are used are from the ERS-2 ( ) and ASCAT ( ) scatterometers. Officially ERA-Interim was going to be compared to the wind products of the SSMI and SSMIS radiometers ( ) but this dataset turned out to contain a lot of incorrect measurement and has a poor representation for high winds. The 10 m height wind products of ERA-Interim are compared to the 10 m height wind products of the ERS-2 and ASCAT scatterometer in 2 ways. With the first way every measured windspeed between 60 N and 60 S is compared to four different representation in ERA-Interim. These four representations are determined using time and spatial interpolations or using the forecast time of ERA-Interim closest to the measurement s time and the windspeed of ERA-Interim closest to the measured windspeed. These comparisons indicate that ERA-Interim had an average lower windspeed than the ERS-2 and ASCAT windspeeds. To take a better look on ERA-Interim s representations for tropical hurricanes the tropical hurricanes measured by ERS-2 and ASCAT are compared to their representations in ERA-Interim. The hurricanes first are filtered and then their representations in the measurements are compared to their representations in ERA-Interim. For the representation of a hurricane the average value of the five highest windspeeds is used. From this comparison the average differences between the representations in ERA-Interim and the measurements are derived. This comparison indicates that ERA-Interim underestimates the hurricanes measured by ERS-2 slightly more than the hurricanes measured by ASCAT. However the standard deviation of these average differences have a wide overlap so no significant difference in time can be determined. To compare the measured hurricanes to their representations in ERA-Interim in a different way the ratio of the number of representations in ERA-Interim relative to the total number of hurricanes with a windspeed higher than 20 m/s is determined. This comparisons indicates that ERA-Interim represents the hurricanes measured by ERS-2 more often above 20 m/s than the hurricanes measured by ASCAT. However the standard deviations of these average ratios have a wide overlap so again no significant difference of the validity of ERA-Interim over the time can be determined. In the end several comparisons between ERA-Interim and the satellite observations are performed but no significant difference for the validity of ERA-Interim for tropical hurricanes over a course of ten years is found. iii

5 Summery (NL) In dit onderzoek word de geldigheid van ERA-Interim voor tropische orkanen onderzocht als functie van de tijd. ERA-Interim is een heranalyse die gebruik maakt van een van de meest recente weermodellen om de toestand van de atmosfeer meerdere malen te voorspellen per dag. Dit betekend dat het enigste wat veranderd aan het model de input metingen zijn die gebruikt worden voor de beginstand van het weermodel. Deze input metingen verbeteren en worden er meer met de tijd. Om te onderzoeken of de geldigheid van ERA-Interim eveneens beter wordt met de tijd worden de windsnelheden die voorspeld zijn door ERA-Interim vergeleken met windmetingen afkomstig van satellietwaarnemingen. Deze satellietwaarnemingen komen van de ERS-2 ( ) en ASCAT ( ) scatterometer. Eigenlijk zouden de metingen van de SSMI en SSMIS radiometers gebruikt worden maar deze bleken erg veel foute metingen te hebben en hebben een slechte representatie voor hoge windsnelheden. The windsnelheden voorspeld door ERA-Interim worden vergeleken met de ERS-2 en ASCAT windmetingen op twee verschillende manieren. Bij de eerste manier wordt elke windmeting tussen 60 N en 60 Z vergeleken met vier verschillende representaties in ERA-Interim. Deze representaties worden bepaald d.m.v. tijd en ruimtelijke interpolaties of door de voorspelling van ERA-Interim te gebruiken die het dichtste bij de tijd van de meting ligt en de windsnelheid in ERA-Interim die het dichtste bij de windsnelheid van de meting ligt. Uit de resultaten van deze vergelijkingen blijkt dat ERA- Interim gemiddeld lagere windsnelheden voorspeld dan de windmetingen van ERS-2 en ASCAT. Om een beter beeld te krijgen van de representaties van tropische orkanen in ERA-Interim worden de representaties van tropische orkanen gemeten door ERS-2 en ASCAT vergeleken met hun representaties in ERA-Interim. Voor de representatie van een tropische orkaan wordt de gemiddelde waarde van de vijf hoogste windsnelheden gebruikt. Als eerst worden de tropische orkanen uit de metingen gefilterd waarna hun representaties worden vergeleken met hun representaties in ERA- Interim. De resultaten die uit deze vergelijkingen komen geven aan dat de orkanen gemeten door ERS- 2 lichtelijk meer worden onderschat dan de stormen gemeten door ASCAT. De standaard deviaties die bij deze gemiddelde verschillen tussen de metingen en ERA-Interim horen overlappen en zijn zo groot dat er geen significant oordeel kan worden getrokken of de geldigheid van ERA-interim voor tropische orkanen is verbeterd of niet. Om de tropische orkanen die gemeten zijn door ERS-2 en ASCAT op een andere manier te vergelijken met hun representaties in ERA-Interim wordt de verhouding tussen het aantal representaties in ERA- Interim groter dan 20 m/s met het totaal aantal orkanen bepaald. Deze vergelijking geeft aan dat de representaties van de tropische orkanen in ERA-Interim vaker een waarde boven de 20 m/s geeft tijdens de metingen van ERS-2 dan tijdens de metingen van ASCAT. Echter overlappen de bijbehorende standaard deviaties en zijn deze zo groot dat er opnieuw geen significant oordeel kan worden getrokken of de geldigheid van ERA-Interim voor tropische orkanen is verbeterd over de tijd. Uiteindelijk zijn er verschillende vergelijkingen uitgevoerd tussen ERA-Interim en de satelliet waarnemingen maar is er geen significante verandering gevonden voor de geldigheid van ERA-Interim voor tropische orkanen tussen 1996 en 2014 iv

6 Table of contents Abbreviations...ii Summery (EN)... iii Summery (NL)... iv 1 Introduction The atmospheric model and satellite observations ERA-Interim Scatterometer winds Radiometer winds Methods Global comparisons Time interpolation Spatial interpolation High wind comparison Results Comparing ERA-Interim and SSMI/SSMIS Global comparisons Comparing ERA-Interim and ERS Global comparisons High wind comparison Comparing ERA-Interim and ASCAT Global comparisons High wind comparison The validity of ERA-Interim over the course of time Conclusion and discussion Recommendations References Appendix A The results of the global comparisons of ERA-Interim with the satellite observations 23 Appendix B Examples of the incorrent measurements of SSMI and SSMIS Appendix C Examples of compared hurricanes between ERS-2 and ERA-Interim Appendix D Examples of compared hurricanes betwee ASCAT and ERA-Interim v

7 1 Introduction The weather is extremely important in everyday life because it can influence for example the agriculture, sea level, aviation and shipping. Because of this it is important to forecast the weather with a high accuracy so all kind of outdoor activities have the time to adjust to it. With forecasting the weather different meteorological parameters occur like the airtemperature, oceantemperature, humidity and windspeed. The KNMI focusses on forecasting these different parameters by creating and improving various international weather prediction models. To improve these weather prediction models it is important to test the validity of them. In this research the validity of ERA-Interim is tested on its representation for tropical hurricanes and extreme winds. ERA-Interim is a reanalysis 1 which is used at the KNMI for climate research and consists of reforecasts which are all modelled with the same weather prediction model. Thus the only thing that changes in the model during the period of the reanalysis are the input measurements that are used as initial situation for the weather prediction model. These input measurements will vary in amount and quality and should improve with time, which indicates that ERA-Interim should improve with time to. The validity of ERA-Interim is tested by comparing the windspeeds predicted by the model to windspeed measurements derived from satellite observations, which are only available above the ocean surface. The windspeeds derived from satellite observations are used in this research because they cover a relatively big amount of the ocean surface (see Figure 1.1). The satellite observations consists of windspeed measurements derived from three different instruments all covering a different period of time. To test the validity of ERA-Interim, several methods are created to compare ERA-Interim to the satellite observations, using the computer language Python. The most useful comparisons and their outcomes will be used to produce the final results. To study the performance of the model with time the results of these comparisons for the different sets of satellite observations can be compared with each other because they all cover a different period of time. The atmospheric model ERA-Interim and the satellite observations will be discussed in paragraph 2, the different methods of comparing in paragraph 3, the results in paragraph 4 and paragraph 0, 5, and 6 will consist of the discussion, conclusion and recommendations. Figure 1.1: An example of the surface covered in one day by one of the used instruments (dark red) plotted over the windspeeds modelled by ERA-Interim. The surface covered by the instrument is given a darker colour so it is easily distinguished. 1 This reanalysis is a dataset of reforecasts that represent the atmosphere that corresponds best with all available measurements in a way that is consistent with the laws of physics. 1

8 2 The atmospheric model and satellite observations In this research the 10 m height winds 2 modelled by ERA-Interim are compared to 10 m height wind measurements derived from satellite observations. These measurements come from two different devices which both rely on microwave radiation, the microwave scatterometers ERS-2 and ASCAT and the microwave radiometers SSMI and SSMIS. The ERA-Interim model will be discussed in paragraph 2.1, the used scatterometer measurement in 2.2 and the used radiometer measurements in ERA-Interim The ERA-Interim reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) is the latest global atmospheric reanalysis. It contains the reforecasts of different meteorological surface parameters (including the 10 m height winds over land and ocean surface) and covers the period from 1979 to present. ERA-Interim calculates these reforecasts by combining one of the leading numerical weather prediction models from ECMWF with an advanced data-assimilation system [1, 2]. This data-assimilation system provides measurements since 1979 for a large variety of meteorological surface parameters. ERA-Interim uses these measurements for the calculations of the reforecasts by extrapolating them in a way it agrees with the laws of physics using the weather prediction model. This results in gridded data products of the reforecasted representations of the atmosphere for i.e. the windspeed, temperature and pressure with a grid size of about 80 by 80 km. ERA-Interim calculates several forecasts per day. The reforecasts tested in this research are for 3, 6, 9, 13, 15 and 18 hours ahead in time at 00:00 AM and 12:00 AM [2]. 2.2 Scatterometer winds A scatterometer is an active radio instrument which is attached to a satellite. It is used to measure the windspeed and direction at 10 meter height above sea surface. It does so by measuring the sea surface roughness from different look angles for different incidence angles, which is related to the windspeed and direction. A scatterometer determines the sea surface roughness by illuminating the sea surface with microwave radiation and measuring the backscattered radiation. This backscattered radiation is closely related to the sea surface roughness because it depends on the magnitude of the capillary waves (see Figure 2.1). Current scatterometers use a frequency of 5.3 GHz (C-Band, which is rather insensitive to rain), or 13.5 GHz (Ku-Band, by which difficulties can occur when measuring during rainfall because it s more sensitive for smaller physical events). A scatterometer measures the backscattered component from three different angles using three antennas which are attached to the satellite. These three antennas generate microwave radar beams 45 forward, sideways and 45 backwards which illuminate a 500 km wide swath, 200 km to the right of the satellite track (see Figure 2.3). As the satellite moves along its orbit, each antenna will measure backscattered components from the sea surface and derives one values per 25 km grid box. Figure 2.1: A schematic representation of microwave scattering on a smooth, slightly rough and rough surface. As the roughness of the surface increases because of an increase in windspeed, more microwave radiation will be backscattered. [3] 2 The windspeed at 10 meter above the surface is often used for models and measurements so they can easily be compared. When the windspeed at a different height is available, the 10 m height windspeed can be determined using a mathematical model. 2

9 This results in three different backscatter measurements per grid box that are separated by a short time delay. With these three backscatter values, the 10 m height windspeed and direction can be determined because the amount of backscattered energy depends on the sea surface roughness as a function of the windspeed and direction [3, 4]. The 10 m height scatterometer winds used in this research are from the European Remote Sensing satellite (ERS-2 scatterometer) from the European Space Agency (ESA) and the Advanced Scatterometer (ASCAT) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). Appendix 1 and 2 consist of examples of the daily coverage of ERS-2 and ASCAT. The ERS-2 wind vector products are produced with the geophysical model function 3 (GMF) CMOD-5 and consist of measurements from 1996 till The ASCAT wind vector products are produced with the GMF CMOD-7 and consists of measurements from 2007 till The only difference between CMOD-5 and CMOD-7 their sensitivity for windspeeds below 5 m/s, which aren t compared in this research. Both the ERS-2 and ASCAT measurement have a grid size of 25 km (see Figure 2.2) and use C-Band. ERS-2 measures with 3 antennas (like in Figure 2.2) and ASCAT with 6, this enables ASCAT to illuminate two swaths instead of one, one 200 km to the left and one 200 km to the right of the satellite track. Because of this ASCAT will measure the windspeed and direction over twice as much surface. 2.3 Radiometer winds A microwave radiometer is a passive instrument which is mostly used for meteorological or oceanographic remotesensing. It determines different meteorological parameters like the windspeed above sea surface, sea surface temperature and the rainfall by measuring the Brightness Temperature (T B ) for different wavelengths [6]. T B is a measure for the radiation traveling upward from the top of the atmosphere and is expressed in units of the temperature of an equivalent black body (Kelvin). Above the ocean surface, T B can broadly be described as the sum of the radiation emitted by the atmosphere (T atm ), emitted by the ocean surface (e T s ) and reflected by the ocean surface ((1 e) T sky ) (see Figure 2.5): T B = e T s + T atm + (1 e) T sky. (1) Figure 2.3: A schematic representation of the geometry of the scatterometer. [5] Figure 2.2: An example of the windspeed measured by the ERS-2 scatterometer. Figure 2.5: A schematic representation of the radiation which enters and leaves the atmosphere above ocean surface. [7] Figure 2.4: An example of the windspeed measured by the SSMI radiometer. 3 A geophysical model function is a function which retrieves the (in this case) windspeed from the measured backscatter components. 3

10 In this equation e represents the emissivity. For sea water in particular, the emissivity depends on different parameters including the sea surface temperature, rainfall and the roughness of the medium s surface, which depends on the windspeed [7]. Radiometers derive the windspeed above ocean surface, the rainfall and other products by measuring T B for different wavelengths. The 10 m height radiometer winds used in this research are from the SSMI (Special Sensor Microwave Imager) and SSMIS (Special Sensor Microwave Imager Sounder) from the RSS (Remote Sensing Systems). RSS uses a physically based algorithm which scales it to a daily uniform grid. This results in data mapped to 0.25 degree grid cells separated by the ascending and descending orbit of the satellite (see Figure 2.4) and is available since

11 3 Methods As told in the introduction, the datasets can be compared in different ways. In this research different ways of comparing are implemented and executed from which the most useful are used for the final result. These used comparisons consist of five different methods and are divided into two groups, four methods in the group global comparisons and one method in the group high wind comparison. To compare the 10 m height winds modelled by ERA-Interim with the 10 m height winds derived by satellite observations (SSMI / SSMIS, ERS-2 or ASCAT) the datasets first have to be downloaded and processed. Downloading, processing and comparing the datasets is done with the use of the computer language Python. The reason that Python is used is because Python allows the user to work with big amounts of data in a convenient way. The ERA-Interim dataset is downloaded which results in 12 global 10 m height wind reforecast per day, 6 forecasts (3, 6, 9, 13, 15 and 18 hours ahead in time) at 00:00 AM and 6 at 00:00 PM, with a resolution of about 80 by 80 km. The downloaded radiometer dataset results in usable measurements of the 10 m height windspeed and rainfall reprocessed to a regular grid with a resolution of 0.25 by 0.25 and the downloaded scatterometer datasets result in usable measurements of the 10 m height windspeed with a grid regular along the satellite orbit and a resolution of 25 by 25 km. For every measured windspeed in the radiometer and scatterometer datasets the time while measuring is available. This means that for every measured windspeed there will be a latitude, longitude, windspeed and time (and rainfall for the radiometer measurements). These will be the variables used in the different comparisons. The four global comparisons will be discussed in paragraph 3.1 and the high wind comparison in paragraph Global comparisons The global comparisons consist of 4 different ways of comparing where the satellite observations will be compared with ERA-Interim per measured windspeed. Because every measured windspeed consists of a latitude, longitude and time, a windspeed with a corresponding latitude, longitude and forecast time can be found in the model. However it rarely happens that the latitude, longitude and time of the measured windspeed match perfectly with the latitude, longitude and forecast time of a grid box in ERA-Interim (see Figure 3.1). Because of the difference between the time of the measurement and the forecast times, the measured windspeed can be compared to the windspeed in ERA-Interim with the closest forecast time or to an interpolation between the previous and next forecast. It is also possible to comparing the measured windspeed to the closest windspeed in ERA- Interim or to an interpolation between the surrounding windspeeds. These interpolation in time and location make it possible to make four different comparisons per measured windspeed: Figure 3.1: An example of the positions of the WVC s measured by ASCAT plotted over the regular grid of the windspeeds modelled by ERA-Interim. Using the forecast time of ERA-Interim closest to the time of the measurement Using the grid box in ERA-Interim closest to the location of the measurement Using the forecast time of ERA-Interim closest to the time of the measurement Using a spatial interpolation between the surrounding windspeeds in ERA-Interim 5

12 Using an interpolation between the last and next forecast time Using the grid box in ERA-Interim closest to the location of the measurement Using an interpolation between the last and next forecast time Using a spatial interpolation between the surrounding windspeeds in ERA-Interim With these four methods the ERA-Interim model will be compared to the satellite observations. All the measured windspeeds within a range of 60 N and 60 S will be compared to their four different representations. This range is used to make sure that the measurements will not be influenced by floating icebergs, which only occur outside these latitudes. In paragraph and below, the used time interpolation and spatial interpolation will be described Time interpolation When the time of the WVC doesn t perfectly match with a forecast time of ERA-Interim, a linear interpolation can be made between the windspeeds of the previous and next forecast. The interpolated value (w i ) can be determined using the ratio of the time of the measurement (t m ) relative to the previous (t 1 ) and next (t 2 ) forecast time: r t = ( t m t 1 t 2 t 1 ) (2) In this equation r t represents the ratio of the time of the measurement relative to the previous and next forecast. Using thus ratio it is possible to determine the interpolated value of the windspeed (w i ): w i = w 1 (1 r t ) + w 2 r t (3) By determining this interpolated value there is only interpolated between values of forecasts with the same model run Spatial interpolation When the location of the measured windspeed doesn t perfectly match with the location of a windspeed in ERA-Interim, a linear interpolation can be made using the surrounding windspeeds in ERA-Interim. Figure 3.2 shows an example of a measured windspeed at coordinates (Lat, Lon) in between the surrounding windspeeds of ERA-Interim at coordinates w 1 (lat max, lon min ), w 2 (lat max, lon max ), w 3 (lat min, lon min ) and w 4 (lat min, lon max ). To determine the interpolated value of the four windspeeds of ERA-Interim, the ratio of the latitude and longitude of the measured windspeed relative to the surrounding latitudes and longitudes must be determined: r lat = Lat Lat min l r lon = Lon Lon min l = Lat l = Lon l (4) (5) Figure 3.2: An schematic representation of a measured windspeed with location (Lat, Lon) positioned in between the surrounding windspeeds of the regular grid of ERA-Interim. 6

13 In these equation, r lat and r lon represent the ratios of the latitudes and longitudes of the measured windspeed relative to the latitudes and longitudes of the surrounding windspeeds in ERA-Interim and l the length of the gridcell. This length is assumed to be constant because there will only be compared between 60 N and 60 S. The interpolated value of the four windspeeds of ERA-Interim can be determined by first interpolating over the longitude between w 1 and w 2 and between w 3 and w 4 : w 1,2 = w 1 r lon + w 2 (1 r lon ) (6) w 3,4 = w 3 r lon + w 4 (1 r lon ) (7) In these equation, w 1,2 and w 3,4 represent the values that are interpolated over the longitude between w 1 and w 2 and between w 3 and w 4. The final interpolated value (w i ) can now be determined by interpolating these values over the latitude: w i = w 1,2 (1 r lat ) + w 3,4 r lat (8) It doesn t matter if the values are first interpolated over the longitude and then over the latitude or the other way around. A comprehensive comparison can be made between one of the satellite observations and ERA-Interim for every method of comparing (or a combination between methods). This comprehensive comparison is done with the use of two-dimensional histograms. In this two-dimensional histogram the measured windspeeds will be plotted against their corresponding windspeeds in ERA-Interim, which can be calculated with the four described methods. By using these 2D histograms it is possible to calculate average differences between the 10 m height winds of the satellite observations and ERA-Interim for the different global comparisons. 3.2 High wind comparison Next to the global comparisons there is the so called high wind comparison. This comparison is used to zoom in on tropical hurricanes and compare their representation in the satellite observations and ERA-Interim. As representation of a hurricane the average of the 5 highest values is used 4. To compare these representations, the hurricanes must first be found in the satellite observations and afterwards compared to their representations in ERA-Interim. Because the position of a hurricane in the satellite observations can differ from its position in ERA-Interim, the centre of the hurricanes will separately be determined both of them. To find these hurricanes, the satellite observations will first be filtered per orbit (so there are no overlapping measurements) on two requirements: They must be between 30 N and 30 S They must be at least 20 m/s By filtering on these two requirements, the filtered winds are assumed as winds that take part in a tropical hurricane. This results in a list of measured windspeeds with their corresponding latitudes, longitudes and times. To determine the centre of the hurricanes in the satellite observations, different steps will be made. At first, the measured windspeeds with a minimum of 20 m/s are sorted from high to low. Then for each of these filtered windspeeds, starting with the highest, a selection will be made of the windspeeds surrounding the filtered WVC within a radius of 800 km 5 (see Figure 3.3). From this selection an average windspeed will be calculated of the 5 windspeeds within the circle with the 4 The average of the 5 highest values is used because it represents a hurricane better than only the highest value, which can be a peak way higher than the other values 5 A radius of 800 km is used because that is considered as the maximum radius of a hurricane. By selecting all the WVC s within this radius it is assured that the highest winds will be included. 7

14 highest windspeed. This average windspeed will be the representation of the hurricane while the highest value will be defined as the centre. To let a group of filtered high winds (above 20 m/s) that lie close to each other count as just one tropical hurricane, there will be no further calculations to the filtered WVC s that were already in a calculation involving them. Because the filtered windspeeds are sorted from high to low the location of the windspeed with the highest value will be defined as centre. To determine the representation of the hurricanes in ERA-Interim the centre of these hurricanes must be determined as well. To determine this centre, a selection is made of the windspeeds in ERA-Interim with the closest forecast time spreading 8 North, South, East and West relative to the centre of the hurricane in the satellite observation. With the windspeeds within this 16 by 16 box the position of the centre of the hurricane will be determined by calculating the average latitude and longitude using the windspeed as weight: lat centre = lat 1 w 1 + lat 2 w 2 + lat n w n w 1 + w 2 + w n (9) lon centre = lon 1 w 1 + lon 2 w 2 + lon n w n w 1 + w 2 + w n (10) In this equation lat, lon and w represent the latitude, longitude and windspeed of the gridcells within the 16 by 16 box. This method is used because hurricanes have a circular shape. This method isn t used for determining the centre of the measured hurricanes because the centre of the hurricane is rarely in the middle of the satellite track (like in Figure 3.3). Then, once again, a selection will be made of the WVC s surrounding the WVC s of ERA-Interim within a radius of 800 km (see Figure 3.3). From this selection an average windspeed will be calculated over the five WVC s with the highest windspeed. Using these methods the representations of the hurricanes in the satellite observations and ERA- Interim can be compared. This comparison is also done with the use of two dimensional histograms. By using 2D histograms it is possible to determine the differences between the representations of hurricanes in the satellite observations and their corresponding representation in ERA-Interim. Figure 3.3: An example of a group of measured WVC s with a minimal windspeed of 20 m/s (left) and its representation in ERA-Interim with the closest forecast time. The cross represents the determined centre and the circle represents the boundary of the 800 km radius. 8

15 4 Results After the ERA-Interim reanalysis and satellite observations are downloaded and processed they can be compared. The datasets are compared in several ways which are described in section 3. The comparisons of ERA-Interim with the SSMI/SSMIS radiometer measurements are discussed in paragraph 4.1, the comparison with the ERS-2 measurements in 0 and the comparison with the ASCAT measurements in 0. Besides the comparisons of ERA-Interim with the satellite observations, the validity of ERA-Interim s representation of hurricanes over the course of time is discussed. This relation is determined by comparing the comparison between ERA-Interim and ERS-2 ( ) with the comparison between ERA-Interim and ASCAT ( ). This is discussed in Comparing ERA-Interim and SSMI/SSMIS The SSMI and SSMIS radiometers have a combined lifespan of 28 years, from 1987 to present. This could have been a really good dataset for determining the validity of the ERA-Interim model over a wide course of time. Unfortunately, the SSMI and SSMIS 10 m height wind products seem to have a very poor representation for high winds. Several visual checks of measured hurricanes show that it has to do with the present rain in hurricanes. Figure 4.2 clearly shows a relation between the rainrate and filtered out wind measurements. On every location where there is even the slightest rainfall (5 mm/h is a relatively low rainrate) the measurements of the windspeeds are filtered out. Because the SSMI and SSMIS datasets do not contain information about why the measurements are filtered out there can be several reasons 6. The relation between the filtered windspeed measurements and the rainrate is examined for several cases like in Figure 4.2. Just like in Figure 4.2 all the windspeed measurement on locations where there is even the slightest rainfall are filtered out. This indicates that the SSMI and SSMIS windspeed measurement are not reliable when measured during rainfall and are filtered out. Next to the filtered out measurements there are also a lot of incorrect measurement. These measurements are almost always located next to filtered out windspeed measurements (see Figure 4.1). Figure 4.1 shows an example of how the high wind comparison spots a hurricane in the SSMI measurements. Several examples like in Figure 4.1 are examined and almost all examples show the same result (more examples can be seen in Appendix B). These incorrect measurements are most likely because of bad processing of RSS. Because of these filtered and incorrect measurements the high wind comparison will not be performed. The global comparisons are performed and are discussed in paragraph Figure 4.2: An example of the windspeed (upper) and rainrate (lower) measured by SSMI in and around a hurricane. Figure 4.1: An example of the windspeed measured by SSMI which is seen as tropical hurricane in the high wind comparison. 6 The flagged SSMI and SSMIS measurements should consist of information about if the flagged measurements are filtered out because of heavy rain but this information is missing. This is probably because of a mistake of RSS (the company which processes and analyses the measurements). 9

16 4.1.1 Global comparisons The global comparisons are performed over a 10 year course, from 1991 till 2001, where only the SSMI 10 m height windspeed products are used. The whole course of time of SSMI/SSMIS isn t used because this isn t necessary, for the measurements of SSMI/SSMIS aren t reliable. Every measured windspeed within a latitude of 60 and -60 latitude is compared to its representations in ERA-Interim. For every comparison several final numerical results have been determined with the use of two-dimensional histograms. Figure 4.3 shows the 2D histogram derived from the comparison where the forecast times of ERA-Interim closest to the measurements times are used and no spatial interpolation. The results of the other comparisons are showed in Appendix A, Figure A.1, A.2 and A.3. This comparison is used because it uses the best representations for high winds. The representations of high winds are better with this comparison because no interpolations are used. In the middle of Figure 4.3 the average difference with the diagonal (blue line in the left graph of Figure 4.3) is plotted. This diagonal represents the value of the measurements. The average values of ERA-Interim as a function of the measured windspeed are the weighted average values of the histogram s bins perpendicular to the diagonal. Determining the average value over diagonal rows of bins visualizes the difference between the measurements better than when the average is determined over the vertical rows of bins. For these average values the corresponding standard deviations are determined. The number of bins used for making the 2D histograms is 200 by 200. This number is used so the average values are calculated with a high accuracy but not too high. When the number of bins is too high the average value of the Table 4.1: The results of the four global comparisons of ERA-Interim with SSMI over a course of 10 year ( ). windspeed > 0 m/s windspeed > 20 m/s average stdv average stdv Closest forecast, no spatial interpolation Closest forecast time, spatial interpolation Time interpolation, no spatial interpolation Time and spatial interpolation Figure 4.3: The results of the global comparison with the closest forecast times and windspeeds in ERA-Interim. Left the 2D histogram containing al the measured values and their representations in ERA-Interim, in the middle the average difference between the model and the measurements with corresponding standard deviations and right the average differences of all measurements above 0 and 20 m/s in the course of time with corresponding standard deviations. 10

17 diagonal row of bins fluctuates too much, which results in unclear plots. With this 2D histogram the weighted average difference with the diagonal is determined with corresponding standard deviation for all winds and for winds above 20 m/s. The average difference of all winds would have the same value if it would have been calculated over vertical rows of bins, the average difference of winds above 20 m/s not. The 2D histogram shows quite a small average difference with the measurement till ± 15 m/s. After the 15 m/s the difference with the measurement negatively increases together with its standard deviation. This indicates that ERA-Interim does a better job at forecasting the lower winds than forecasting the higher winds. One of the reasons for this decrease in validity are the incorrect measurement of SSMI and SSMIS, as shown in Figure 4.1. These incorrect values can be seen in the lower right corner of the 2D histogram in Figure 4.3 and significantly influence the results of the average values. Because of this the comparison between ERA-Interim and the SSMI and SSMIS radiometers should not be used to test the validity of ERA-Interim for higher winds. The results of the 3 other global comparisons are listed in Table

18 4.2 Comparing ERA-Interim and ERS-2 ERS-2 consist of 10 m height wind products over a course of 6 years, from 1996 to Just like the SSMI and SSMIS datasets, ERS-2 10 m height windspeed products contain incorrect windspeed measurements (see Figure 4.4). However these incorrect measurement are not caused by rain. The filtered measurements surrounding the incorrect measurements indicate that these measurements aren t correctly flagged. However this occurs just a few times per year, making it possible to filter them out manually. Because of this the ERS-2 data can be used for the comparison with ERA-Interim. The global comparisons are discussed in paragraph and the high wind comparison in paragraph Figure 4.4: An example of the incorrect measurements of ERS Global comparisons Figure 4.5 shows the results of the global comparison using the forecast times in ERA-Interim closest to the times of the measurements and no spatial interpolation. In the 2D histogram you can see several vertical lines of values for the measurement (bottom right). These values represent the incorrect values like in Figure 4.4. However these values are just a small amount compared to the rest of the values so Table 4.2: The results of the four different global comparisons of ERA-Interim with ERS-2. windspeeds > 0 m/s windspeeds > 20 m/s average stdv average stdv Closest forecast time, no spatial interpolation Closest forecast time, spatial interpolation Time interpolation, no spatial interpolation Time and spatial interpolation Figure 4.5: The results of the global comparison using the closest forecast times no spatial interpolations. Left the 2D histogram containing al the measured values and their representations in ERA-Interim, in the middle the average difference between the model and the measurements with corresponding standard deviations and right the average differences of all measurements above 0 and 20 m/s in the course of time with corresponding standard deviations. 12

19 they will not significantly alter the results of the comparisons. The visualizations of the other comparisons can be seen in Appendix A, Figure A.4, A.5 and A.6. The reason that the comparison using the closest forecast times and no spatial interpolations is used is because this comparison is most likely to be the most correct, especially for high winds. Because this comparison does not use any interpolation the compared winds of ERA-Interim aren t averaged between two or more values what results in higher winds. The average differences with the diagonal are determined for rows of bins perpendicular to the diagonal. This method is used because it gives a better representations for the higher winds of ERA-Interim. In Figure A.7 in Appendix A you can see the result of the same comparison when the average values are determined over vertical rows of bins. The 2D histogram (left) and average differences (middle) in Figure 4.5 show way smaller differences between the ERA-Interim and ERS-2 winds than the 2D histogram of ERA-Interim and SSMI winds. It shows an average difference which indicates that ERA-Interim underestimates the measured winds by just m/s for all the winds and 0.28 m/s for the winds above 20 m/s (see Table 4.1). The results of the other three global comparisons are also listed in Table 4.1, as expected the comparison using the closest forecast times and no interpolation gives the lowest average difference for winds above 20 m/s. However, the average difference for the winds above 20 m/s have a way bigger standard deviation than the average difference of all the winds. The graph on the right in Figure 4.5 shows the course of these average differences with their standard deviation over the time. It does not show any significant differences over the course of time, neither for the average of all the winds and for the winds above 20 m/s High wind comparison With the high wind comparison the measured hurricanes are filtered out and their representations in ERA-Interim and ERS-2 are compared. These hurricanes are found by filtering the measurements on windspeeds above 20 m/s within 30 N and 30 S. For the representations of the hurricanes the average value of the 5 highest windspeeds is used. Several examples of hurricanes measured by ERS-2 and their representations in ERA-Interim can be seen in Appendix C. In these examples the centre and radius in which the 5 maximum windspeeds are located are visualized, just as in Figure 3.3. Figure 4.6: The results of the high wind comparison between ERA-Interim and ERS-2. Left the 2D histogram containing the representations of the measured and modelled hurricanes, in the middle the average values of the vertical rows of bins and right the average values of the vertical rows of bins over the course of time. 13

20 The 2D histogram left in Figure 4.6 shows the representations of the modelled hurricanes plotted against their measured representations. This histogram contains the compared representations of 587 hurricanes measured by ERS-2. The graph in the middle shows the average differences of the hurricanes representations per vertical row of bins. The average differences are calculated over vertical rows of bins with this comparison because only one average value over the whole 2D histogram will be calculated. The number of bins of the 2D histogram is 20 by 20. This number is used so the graphs don t fluctuate too much and stay visually clear. A higher number of bins does not significantly change the outcome of these results. Just as with the global comparisons, the weighted average difference of all the compared representations combined is calculated. This average difference is m/s with a standard deviation of 2.9 m/s. This average difference is also determined per year with its corresponding standard deviations. These values are plotted in the right graph of Figure 4.7. This graph shows that there are no significant changes in the average difference over the course of 1996 till

21 4.3 Comparing ERA-Interim and ASCAT ASCAT consist of 10 m height wind products over a course of 8 years, from 2007 till Unlike the SSMI/SSMIS and ERS-2 datasets, ASCAT doesn t contain incorrect measurements. In paragraph the global comparisons are discussed and in paragraph the high wind comparison Global comparisons Figure 4.8 shows the results of the global comparison between ASCAT and ERA-Interim using the closest forecast times and no spatial interpolations. The results from the other global comparisons with ASCAT can be seen in Appendix A, Figure A.8, A.9 and A.10. The 2D histogram left in Figure 4.8 looks a lot like the 2D histogram of the comparison with ERS-2. To get a numerical results out of this comparison the average difference with the diagonal is determined. Again this average difference is determined for the rows of bins perpendicular to the diagonal. The average difference is plotted in the middle graph of Figure 4.8 as a function of the windspeed of the measurements. The average difference of all winds is determined and so is the average difference of all winds above 20 m/s. These values are listed for all four global comparisons in Table 4.3. As expected the comparison using the closest forecast times and no interpolation gives the lowest average difference for winds above 20 m/s. The average difference of all winds is m/s with a standard deviation of m/s and the average difference of all winds above 20 m/s is m/s with a standard deviation of 1.76 m/s. In the right graph of Figure 4.8 these average differences are plotted over the course of time. As you can see it doesn t show any significant differences over the course of time. Table 4.3: The results of the four different global comparisons of ERA-Interim with ASCAT. windspeeds > 0 windspeeds > 20 average stdv average stdv Closest forecast time, no spatial interpolation Closest forecast time, spatial interpolation Time interpolation, no spatial interpolation Time and spatial interpolation Figure 4.8: The results of the global comparison using the closest forecast times and no spatial interpolations. Left the 2D histogram containing al the measured values and their representations in ERA-Interim, in the middle the average difference between the model and the measurements with corresponding standard deviations and right the average differences of all measurements above 0 and 20 m/s in the course of time with corresponding standard deviations. 15

22 4.3.2 High wind comparison With the high wind comparison the measured hurricanes in ASCAT are filtered out and compared to their representations in ERA-Interim. These hurricanes are found by filtering the measurements on windspeeds above 20 m/s within 30 N and 30 S. For the representations of the hurricanes the average value of the 5 highest windspeeds is used. Several examples of hurricanes measured by ASCAT and their representations in ERA-Interim can be seen in Appendix D. Figure 4.9 shows the results from the high wind comparison between ASCAT and ERA-Interim. The 2D histogram left in Figure 4.9 contains the compared representations of 1711 hurricanes measured by ERS-2. The middle graph of Figure 4.9 shows the average difference with the diagonal (plotted blue in the left graph of Figure 4.9). Again the number of bins used is 20 by 20. The weighted average difference of all winds combined is calculated with its corresponding standard deviation. This results in an average difference of m/s with a standard deviation of 2.65 m/s. This average difference is also calculated per year and plotted in the right graph of Figure 4.9. It looks like the average differences slightly decrease. However no significant conclusion can be made because the standard deviations overlap. Figure 4.9: The results of the high wind comparison between ERA-Interim and ERS-2. Left the 2D histogram containing the representations of the measured and modelled hurricanes, in the middle the average values of the vertical rows of bins and right the average values of the vertical rows of bins over the course of time. 16

23 4.4 The validity of ERA-Interim over the course of time To examine the validity of ERA-Interim for tropical hurricanes over the course of time the results from the high wind comparison will be compared. This validity could have been examined over a wide course of time using the SSM/SSMIS radiometer wind product but these measurements aren t reliable for high wind comparisons. Because of this only the comparisons with the ERS-2 and ASCAT scatterometers will be compared. These results may be compared because the ERS-2 and ASCAT 10 m height wind products are derived with the GMF s CMOD-5 and CMOD-7, which only differ in windspeeds below 5 m/s. Figure 4.10: The average differences of the representations of hurricanes in ERA-Interim with their representations in ERS-2 ( ) and ASCAT ( ). The average difference of the hurricanes representations between ERA-Interim and ERS-2 is m/s with a standard deviation of 2.9 m/s and between ERA-Interim and ASCAT is m/s with a standard deviation of 2.65 m/s. These average values indicate that the average difference between ERA-Interim and the measurements decreases, however because of the wide overlapping standard deviations no significant verdict can be made if the validity improved in time. To get a better visualisation of this validity the average differences are calculated per year. In Figure 4.10 these average differences of the representations of the measured hurricanes with their representations in ERA-Interim are plotted over the course of time. It looks like the average differences decreases with time, but again because of the wide overlapping standard deviation this can t be significantly concluded. To get a different perspective on the validity over time a different comparison between the hurricanes representations between ERA-Interim and the measurements is used. In this case the percentage of hurricanes in ERA- Interim with a representations bigger than 20 m/s is determined per year for the comparison between ERA-Interim with ERS-2 and ASCAT. The hurricanes and their representations are derived with the high wind comparison so the same hurricanes will be used as with the high wind comparison. In Figure 4.12 the percentage of representations in the model with a value higher than 20 m/s is plotted per year for the comparison with ERS-2 and ASCAT. This figure indicates that ERA-Interim estimates the hurricanes higher during the time range of ERS-2 than during the time range of ASCAT. The weighted average value of the ratio of hurricanes with a representation in ERA-Interim bigger than 20 m/s during the 17

24 time range of ERS-2 is 36% with a corresponding standard deviation of 56% and during the time range of ASCAT 23% with a standard deviation of 16%. These average values indicate that ERA-Interim estimates the filtered hurricanes with higher maximum windspeeds but because of the large standard deviation of the average value during the time range of ERS-2 this can t be concluded with a large certainty. A reason that ERA-Interim estimates the hurricanes higher during the time range of ERS-2 than during the time range of ASCAT can be because the hurricanes measured by ERS-2 are larger and more intense than the hurricanes measured by ASCAT. This is possible because the intensity of hurricanes can differ per year. To examine this the representations of the hurricanes measured by ERS- 2 and ASCAT are compared. These representations are compared by determining the ratio of the number of hurricanes as function of the windspeed. The reason that the ratio of the total amount of hurricanes is used is because ASCAT measured over a longer period of time and covers more ocean surface than ERS-2, what results in more measured hurricanes. Figure 4.11 shows this ratio for the hurricanes measured by ERS-2 and ASCAT. It shows that the ratios almost perfectly match at every windspeed, what indicates that ERS-2 and ASCAT relatively measure the same amount of hurricanes with different intensities. This means it can be excluded that the reason that ERA-Interim estimates the measured hurricanes higher during the time period of ERS-2 than during the time period of ASCAT because the hurricanes measured by ERS-2 are more intense than the hurricanes measured by ASCAT. Figure 4.12: The percentage of hurricanes with a representation in ERA-Interim bigger than 20 m/s. Figure 4.11: The ratio between the number of hurricanes and the total amount of hurricanes as a function of the windspeed of their representations. 18

25 5 Conclusion and discussion The goal of this research was to determine if the validity of ERA-Interim for tropical hurricanes would improve over the course of time. This validity should improve because the input measurements ERA- Interim uses to calculate the forecasts improve in amount and quality with time. To test this validity the 10 m height wind products modelled by ERA-Interim are compared to the 10 m height wind product derived from satellite observations. The used 10 m height windspeed measurements are derived from the SSMI and SSMIS radiometer and ERS-2 and ASCAT scatterometer. After analysing the windspeed measurements from the three datasets the SSMI/SSMIS dataset turned out to have a very poor representation of hurricanes and almost all high windspeed measurements are filtered out. The reason for this is because the wind products from the SSMI and SSMIS radiometers confuse high windspeeds with rainfall thus they are filtered out. Besides these incorrect measurements almost all windspeeds measured during rainfall are filtered out. The reason for this is most likely because the wind products aren t reliable when the windspeed is measured during rainfall. Because the SSMI/SSMIS dataset turned out to be useless the validity of ERA-Interim for hurricanes over the course of time will be examined using only the ERS-2 and ASCAT dataset. The windspeed measurements from the satellite observations are compared to their direct representations in ERA- Interim using the global comparisons where the closest forecast times in ERA-Interim are used and no spatial interpolation. Table 5.1 lists the results of these comparisons. Table 5.1: The average differences between ERA-Interim and ERS-2/ASCAT for all winds and only for winds above 20 m/s. Average > 0 [m/s] Stdv > 0 [m/s] Average > 20 [m/s] Stdv > 20 [m/s] ERS ASCAT These comparison show a smaller average difference between ERA-Interim and ASCAT than between ERA-Interim and ERS-2. The corresponding standard deviations of the average differences are relatively big and have a wide overlap. Because of this wide overlap no significant conclusion can be made if ERA- Interim has a better representation for high winds during the time range of ERS-2 or during the time range of ASCAT. What can be said (because of the smaller average differences with ASCAT) is that ERA- Interim consist of higher winds during the period of ASCAT than during the period of ERS-2. However this can be caused due to climate changes. To take a better look on ERA-Interim s representations for tropical hurricanes the tropical hurricanes measured by ERS-2 and ASCAT are compared to their representations in ERA-Interim. The hurricanes first are filtered using the high wind comparison and then their representations in the measurements are compared to their representations in ERA-Interim. From this comparison the average differences between the representations in ERA-Interim and the measurements are derived. From the high wind comparison with ERS-2 an average difference of m/s with a standard deviation of 2.9 m/s is derived and from the high wind comparison with ASCAT an average difference of m/s with a standard deviation of 2.65 m/s is derived. There is a small decrease between the average difference with ERS-2 and ASCAT but because of the overlapping standard deviations there can t be concluded that hurricane s representations in ERA-Interim got better with time. Also these average differences with corresponding standard deviations are determined per year but no significant change in time can be concluded because of the overlapping standard deviations. 19

26 To compare the representations of the hurricanes between ERA-Interim and the measurements in another way, the percentage of the number of the hurricanes representations in ERA-Interim with a value above 20 m/s is determined per year. For the comparison with ERS-2 36% percent of the representations in ERA-Interim has a value higher than 20 m/s and a standard deviation of 55%. For the comparison with ASCAT 23% of the representations in ERA-Interim has a higher value than 20 m/s and a standard deviation of 16%. To exclude that the reason for this difference is that the hurricanes measured by ERS-2 are more intense than the hurricanes measured by ASCAT these hurricanes are compared. This comparison is shown in Figure 4.11 and indicates that the ratio of the number of hurricanes with a certain intensity match, what means that the intensities of the hurricanes measured during ERS-2 and ASCAT don t differ from each other. The high wind comparisons of ERA-Interim with ERS-2 and ASCAT results in a smaller average difference between the representations of the hurricanes of ASCAT and ERA-Interim than between the representations of the hurricanes of ERS-2 and ERA-Interim. A reason for this can be because ASCAT is a more recent measuring device than the ERS-2 so its wind product are better. However no significant conclusion can be made with these results because the corresponding standard deviation have a wide overlap. The comparison between the hurricanes representation tell that relatively more hurricanes have a representation in ERA-Interim above 20 m/s during the time period of ERS-2 than during the time period of ASCAT. However just like with the high wind comparisons the standard deviations have a wide overlap so no significant conclusion can be made. Unfortunately because of these wide overlapping standard deviations in the results no significant verdict can be made if the validity of ERA- Interim for tropical hurricanes is improved or got worse over the course of time. However, looking at the high wind comparisons between ERA-Interim and ERS-2/ASCAT, it is clear that ERA-Interim under estimates almost all hurricanes. 20

27 6 Recommendations The goal of this research was to examine if the validity of ERA-Interim for tropical hurricane over the course of time improved. Unfortunately no significant change of this validity has been found. To try and find if there is a change in validity over the course of time, several proceedings can be added to this research. One recommendation is to seek for a way to get the SSMI/SSMIS measurements useful because the SSMI/SSMIS radiometers were active over a wide period of time. However this would be a really big project because the current GMF used to derive the SSMI/SSMIS wind product should be improved, which is difficult and takes a lot of time. Another way to get measurements over a wider period of time is to use the windspeed measurements of ERS-1 ( ) and QuikSCAT (Quick Scatterometer) ( ). Adding these wind products to the research results in a wider range of time over which the validity of ERA-Interim for tropical hurricanes can be determined. Also there will be some overlapping periods of time which makes it possible to compare measurements that are valid over the same period of time. Because of this a better representations of the differences between the wind products derived from the different measuring devices can be made. Another recommendation is to use other representations for the hurricanes. In this research average value of the five highest windspeeds in a hurricane is used as representations. A way to compare the hurricanes on another way is to use another representation for the hurricanes. For these representations other parameters can be used like the pressure within a hurricane or the temperature of the seawater before and after the hurricane. The pressure differences between the pressure of the centre of the hurricane and outside of the hurricane can be used as a measure for the intensity of the hurricane. Also the pressure can be used to determine the centre of the hurricane with a higher accuracy. Unfortunately the only way to measure the pressure within a hurricane is via buoys, which have a relatively low density over the ocean surface. The temperature of the seawater before and after the hurricane has passed it can be used to determine the amount of energy a hurricane takes from the seawater. This amount of energy gives a representation of the intensity of a hurricane. 21

28 7 References [1] ERA-Interim - ECMWF Accessed on October [2] The ERA-Interim reanalysis: configuration and performance of the data assimilation system October [3] ERS Scatterometer Product User Manual Accessed on November [4] Scatterometers Accessed on November [5] Scatterometer Principle - Scatterometer Accessed on November [6] Brightness Temperature - Remote Sensing Systems Accessed on October [7] WindSat - Remote Sensing of Ocean Surface Winds - U.S. Naval Research Laboratory Accessed on October

29 Appendix A The results of the global comparisons of ERA-Interim with the satellite observations Figure A.1: Global comparison with SSMI using the closest forecast time and spatial interpolations. Figure A.2: Global comparison with SSMI using time interpolations and no spatial interpolations. 23

30 Figure A.3: Global comparison with SSMI using time and spatial interpolations. Figure A.4: Global comparison with ERS-2 using the closest forecast time and spatial interpolations. 24

31 Figure A.5: Global comparison with ERS-2 using time interpolations and no spatial interpolations. Figure A.6: Global interpolation with ERS-2 using time and spatial interpolations. 25

32 Figure A.7: Global comparison with ERS-2 using the closest forecast time and no spatial interpolations. In this case the average values per row of bins are calculated over the vertical rows of bins. Figure A.8 Global comparison with ASCAT using the closest forecast time and spatial interpolations. 26

33 Figure A.9: Global comparison with ASCAT using time interpolations and no spatial interpolations. Figure A.10: Global comparison with ASCAT using time and spatial interpolations 27

34 Appendix B Examples of the incorrent measurements of SSMI and SSMIS Figure B.1: An example of the incorrect measurements derived from the SSMI radiometer. Figure B.2: An example of the incorrect measurements derived from the SSMI radiometer. Figure B.3: An example of the incorrect measurements derived from the SSMI radiometer. Figure B.4: An example of the incorrect measurements derived from the SSMI radiometer. 28

35 Appendix C Examples of compared hurricanes between ERS-2 and ERA-Interim Figure C.1: An example of a hurricane that is compared between ERS-2 and ERA-Interim with the high wind comparison where ERA-Interim gives a good presentation of the hurricane Figure C.2: An example of a hurricane that is compared between ERS-2 and ERA-Interim with the high wind comparison where ERA-Interim underestimates the hurricane Figure C.3: An example of a hurricane that is compared between ERS-2 and ERA-Interim with the high wind comparison where 29

36 Appendix D Examples of compared hurricanes betwee ASCAT and ERA-Interim Figure D.1: An example of a hurricane that is compared between ASCAT and ERA-Interim with the high wind comparison where ERA-Interim gives a good presentation of the hurricane Figure D.2: An example of a hurricane that is compared between ASCAT and ERA-Interim with the high wind comparison where ERA-Interim underestimates the hurricane. Figure D.3: An example of a hurricane that is compared between ASCAT and ERA-Interim with the high wind comparison where it s unclear if it s an actual tropical hurricane or some other weather system. 30