Preliminary validation of ocean surface vector winds estimated from China’s HY-2A scatterometer

The microwave scatterometer on the Haiyang-2A (HY-2A) satellite is designed to provide global sea surface wind field data. The accuracy of HY-2A scatterometer wind retrievals is determined through various comparisons with moored buoys and the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data. These comparisons were made in wide regions, including open sea and coastal areas, over a four-month period from January to March 2012 and August 2012. The retrieved wind speed results agree well with in situ observations and model data with respective biases ?0.19 m s^?1 and 0.01 m s^?1 and root mean square error 2.02 m s^?1 and 1.81 m s^?1. However, the wind direction errors are a little higher. The overall bias and root mean square deviation of wind direction are ?2.24°, 1.74°, and 40.28°, 38.56°, respectively. The wind speed and direction residuals are higher in low- and high-wind speed ranges. In addition, the wind speed and direction are relatively more accurate for open sea than those in coastal regions.     

Monitoring intense dust storms over the Indian region using satellite data – a case study

Dust storms are normally considered to be natural hazards. During such events, dust aerosol is loaded into the atmosphere, directly reducing visibility and effectively reflecting solar radiation back to space. In the present study, an intense dust storm was monitored during the first week of June 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua data over the Indian region. A dust cloud was detected using a combination of MODIS reflective and emissive channels and moving trace/spread monitored by its multi-temporal data. The MODIS Terra-derived aerosol optical depth at 550 nm (AOD₅₅₀) and the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI) were used in conjunction with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis wind fields for the monitoring of dust clouds. The study reveals that the movement of a high concentration of dust clouds coincided with the NCEP/NCAR reanalysis meridional and zonal wind fields (>8 m s^?1) at pressure levels of 700 hPa. The Cloud–Aerosol Lidar Pathfinder Satellite Observations (CALIPSOs) that derive vertical feature mask images also suggested that the vertical extent of the dust aerosol layer was at a height of about 6 km over northern India on 2 June 2010. The roles of long-range transport of dust over the entire Gangetic plane are analysed using back trajectories from the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Back trajectory analysis suggests that dust clouds moving over long distances entered from the western side of India on 1 June 2010.     

Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses

Scatterometer surface wind speed and direction observations in combination with radiometer wind speeds allow to generate surface wind analyses with high space and time resolutions over global as well as at regional scales. Regarding scatterometer sampling schemes and physics, the resulting surface wind analyses suffer from lack of accuracy in areas near coasts. The use of the synthetic aperture radar (SAR) onboard the Sentinel-1A satellite attempts to address the enhancement of surface wind analyses issues. In this study, SAR wind speeds and directions retrieved from backscatter coefficients acquired in interferometric wide (IW) swath mode are used. Their accuracy is determined through comprehensive comparisons with moored buoy wind measurements. SAR and buoy winds agree well at offshore and nearshore locations. The statistics characterizing the comparison of SAR and buoy wind speeds and directions are of the same order as those obtained from scatterometer (Advanced SCATterometer (ASCAT) and RapidScat) and buoy wind comparisons. The main discrepancy between SAR and buoy data are found for high wind speeds. SAR wind speeds exceeding 10 m s^–1 tend to be underestimated. A similar conclusion is drawn from SAR and scatterometer wind speed comparisons. It is based on the underestimation of SAR backscatter coefficient (σ°) with respect to σ° estimated from scatterometer winds and the geophysical model function (GMF) named CMOD-IFR2 (Ifremer C band MODel). New SAR wind speeds are retrieved using CMOD-IFR2. The corrected SAR retrievals allow better determination of the spatial characteristics of surface wind speeds and of the related wind components in near-coast areas. They are used for enhancing the determination of the spatial structure function required for the estimation of wind fields gridded in space and time at the regional scale. The resulting wind fields are only determined from scatterometer wind observations in combination with radiometer retrievals. Their qualities are determined through comparisons with SAR wind speeds and directions, and through their application for determination of wind power off Brittany coasts.     

The impact of assimilation of satellite derived wind observations for the prediction of a monsoon depression over India using a mesoscale model

The Penn State/NCAR mesoscale model (MM5) has been used in this study to ingest and assimilate the INSAT‐CMV (Indian National Satellite System‐Cloud Motion Vector) wind observations using analysis nudging (four‐dimensional data assimilation, FDDA) to improve the prediction of a monsoon depression which occurred over the Bay of Bengal, India during 28 July 2005 to 31 July 2005. To determine the impact of assimilation of INSAT‐CMV winds on the prediction of a monsoon depression, three sets of numerical experiments (NOFDDA, FDDA and FDDA CMV) were designed. While the FDDA CMV run assimilated satellite derived winds only, the FDDA run assimilated both satellite and conventional observations. The NOFDDA run used neither satellite nor conventional observations. The results of the study indicate that the simulated sea level pressure field from the FDDA run is more consistent with the sea level pressure field from NCEP‐FNL compared to the FDDA CMV and NOFDDA runs. The highest correlation and lowest rms error of the sea level pressure field are associated with the FDDA run, and this provides a quantitative verification of the improvement due to the assimilation of satellite derived winds and the conventional upper air observations for the prediction of monsoon depression. All the three model simulated winds are in good agreement with the analysis winds at 850 hPa, 500 hPa and 200 hPa levels. The simulated structure of the spatial precipitation pattern for the assimilation experiments (FDDA and FDDA CMV) are closer to the TRMM observations with more rainfall simulated over the east coast regions in the assimilation experiments. The rms errors of the wind speed for the FDDA run show lower values at 500 hPa for all the three model runs, with a reduction in all three levels of up to 0.8–1.4 m s^?1 for the FDDA run and 0.5–1.9 m s^?1 for the FDDA CMV run with respect to the NOFDDA run. The statistical significance of the sea level pressure and the precipitation differences between the FDDA and the NOFDDA as well as the differences between the FDDA CMV and the NOFDDA have been calculated using the two‐tailed Student's t‐test and were found to be statistically significant. The influence of varying the nudging coefficients in the FDDA experiment has been studied.     

Inter-comparison of wind profiles in the tropical boundary layer remotely sensed from GPS radiosonde and Doppler wind lidar

Winds play a very important role in the dynamics of the lower atmosphere, and there is a need to obtain vertical distribution of winds at high spatio-temporal resolution for various observational and modelling applications. Profiles of wind speed and direction obtained at two tropical Indian stations using a Doppler wind lidar during the Indian southwest monsoon season were inter-compared with those obtained simultaneously from GPS upper-air sounding (radiosonde). Mean wind speeds at Mahbubnagar (16.73° N, 77.98° E, 445 m above mean sea level) compare well in magnitude for the entire height range from 100 m to 2000 m. The mean difference in wind speed between the two techniques ranged from ?0.81 m s^?1 to +0.41 m s^?1, and the standard deviation of wind speed differences ranged between 1.03 m s^?1 and 1.95 m s^?1. Wind direction by both techniques compared well up to about 1200 m height and then deviated slightly from each other at heights above, with a standard deviation in difference of 19°–48°. At Pune (18^○32′ N, 73^○51′ E, 559 m above mean sea level), wind speed by both techniques matched well throughout the altitude range, but with a constant difference of about 1 m s^?1. The root mean square deviation in wind speed ranged from 1.0 to 1.6 m s^?1 and that in wind direction from 20° to 45°. The bias and spread in both wind speed and direction for the two stations were computed and are discussed. The study shows that the inter-comparison of wind profiles obtained by the two independent techniques is very good under conditions of low wind speeds, and they show larger deviation when wind speeds are large, probably due the drift of the radiosonde balloon away from the location.     

Speed ambiguity in hurricane wind retrieval from SAR imagery

Under strong ocean surface wind conditions, the normalized radar cross section of synthetic aperture radar (SAR) is dampened at certain incident angles, compared with the signals under moderate winds. This causes a wind speed ambiguity problem in wind speed retrievals from SAR, because two solutions may exist for each backscattered signal. This study shows that the problem is ubiquitous in the images acquired by operational space‐borne SAR sensors. Moreover, the problem is more severe for the near range and range travelling winds. To remove this ambiguity, a method was developed based on characteristics of the hurricane wind structure. A SAR image of Hurricane Rita (2005) was analysed to demonstrate the wind speed ambiguity problem and the method to improve the wind speed retrievals. Our conclusions suggest that a speed ambiguity removal algorithm must be used for wind retrievals from SAR in intense storms and hurricanes.     

A geometrical optics model based on the non-Gaussian probability density distribution of sea surface slopes for wind speed retrieval at low incidence angles

In this study, a large amount of data from precipitation radar (PR) and National Data Buoy Center (NDBC) buoys are collocated for the development and validation of a Geometrical Optics Model, in order to retrieve wind speed at small incidence angles. The omni-directional model is developed based on the combination of the quasi-specular scattering theory and non-Gaussian probability density distribution of ocean surface slope, and can be applied at incidence angles as high as 15°. There are four parameters included in the proposed model: the effective Fresnel reflection coefficient, the mean square slope, and the two coefficients associated with the kurtosis of the sea surface slope distribution. Using one half of the collocated data, the dependence of the four parameters on the in situ wind speed is acquired. The results show that the effective Fresnel reflection coefficient has a decrease relative to that obtained in previous studies. We combine the proposed model with the maximum-likelihood estimation (MLE) technique to retrieve the ocean surface wind speed at the 10 m height. The retrieved wind speeds are then validated against those measured by the NDBC buoys. The comparison shows that the root mean square error (RMSE) and bias between the model retrievals and buoy observations are 1.54 m s^–1 and 0.1 m s^–1, respectively, revealing high agreements in the wind speed estimations. The results of this study indicate that the proposed model and the PR measurements at low incidence angles can provide reasonably accurate estimates of the surface wind speeds within the range of 0–20 m s^–1.     

Variability and trends of mean maximum and mean minimum air temperature in Greece from ground-based observations and NCEP–NCAR reanalysis gridded data

In this study, the variability and trends of the mean annual and seasonal maximum and minimum surface air temperature in Greece are examined, using monthly data sets of 26 meteorological stations from the Hellenic National Meteorological Service (HNMS) and gridded data from the National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis project for the period 1955–2001. NCEP–NCAR reanalysis data sets are created by assimilating climate observations from different sources, including ships, satellites, ground stations, radiosonde observations and radar. The general purpose of conducting reanalyses is to produce multi-year global state-of-the-art gridded representations of atmospheric states, generated by a constant model and a constant data-assimilation system.

The trends of the mean extreme air temperatures were evaluated, using the Mann–Kendall criterion, and, in the process, factor analysis was applied to both the stations' and the NCEP–NCAR grid points time series. Regarding the mean maximum air temperature, the first main factor, which explains a high percentage of the total variance, presents a statistically significant (CL?=?95%) negative trend only during the winter. The first main factor of the mean minimum air temperature manifests a statistically significant (CL?=?95%) positive trend during the summer and the year, and a statistically significant (CL?=?95%) negative trend in autumn and winter. These findings were compared to the respective ones of the NCEP–NCAR reanalysis data and significant differences in the spatial distribution and temporal variability of the extracted new factors were found. These differences between the two examined data sets could be attributed to topographical factors such orography and land–sea distribution, which could not be represented properly by the reanalysis model.     

Comparison of Oceansat-2 scatterometer- to buoy-recorded winds and spatial distribution over the Arabian Sea during the monsoon period

For this wind resource assessment (WRA) study, wind speed and direction are the fundamental inputs. Also, these studies are data driven and require large historical wind speed data sets available on the site. This work explores the application of space-based scatterometer winds for assimilation into WRA studies towards the development of offshore wind energy. This article focuses on estimating the performance of Oceansat-2 scatterometer (OSCAT)-derived wind vector using in situ data from buoys at different locations in the Arabian Sea. A comparative study between three methods for estimating the equivalent neutral winds (ENW) for buoys is carried out. OSCAT winds were closest to ENW estimated by the Liu–Katsaros–Businger (LKB) method. The spatial and temporal windows for comparison were 0.5° and ±60 minutes, respectively. The monsoon months (June–September) of 2011 were selected for study. The root mean square deviation for wind speed is less than 2.5 m s^?1 and wind direction is less than 20°, and a small positive bias is observed in the OSCAT wind values. From the analysis, the OSCAT wind values are consistent with in situ-observed values. Furthermore, wind atlas maps were developed with OSCAT winds, representing the spatial distribution of winds at a height of 10 m over the Arabian Sea.     

Wind resource assessment from C-band SAR

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. First, wind speeds retrieved from a series of 91 ERS-2 SAR and Envisat ASAR images, at moderate wind speeds (2-15 m s^− 1), were validated against in situ measurements from an offshore mast in the North Sea. The wind direction input, necessary for SAR wind speed retrievals, was obtained from the meteorological mast and from a local gradient analysis of wind streaks in the SAR images. A wind speed standard deviation of ∼ 1.1 m s^− 1 was found when in situ wind directions were used. The use of local gradient wind directions yielded a standard deviation of ∼ 1.3 m s^− 1. Wind speeds retrieved from three geophysical model functions (CMOD-IFR2, CMOD4, and CMOD5) were compared. The best approximation to the in situ measurements of wind speed was found for CMOD-IFR2, despite a bias on the order of − 0.3 m s^− 1. CMOD4 retrievals also underestimated the wind speed, whereas the bias on CMOD5 retrievals was negligible. Then, wind resource assessments were made from the SAR-based wind observations to show how errors in wind speed from the different SAR wind retrievals were reflected in the wind statistics. The mean wind speed, obtained for all of the 91 SAR scenes, was linked closely to the bias of SAR wind retrievals. Agreement to ± 15% of the in situ measurements was found for all the wind retrieval methods tested. The accuracy of power density estimates for the entire data set was evaluated by the standard deviation of SAR wind retrievals relative to the in situ measurements. SAR wind fields retrieved with CMOD-IFR2, using in situ wind direction inputs, exactly yielded the power density predicted from in situ measurements alone. The SAR-based wind resource assessment also corresponded well to predictions from longer time series of in situ measurements. This indicates that a reliable wind resource assessment may be achieved from a series of randomly selected SAR images. The findings presented here could be useful in future wind resource assessment based on SAR images.     

Validation of Kalpana-1 atmospheric motion vectors against upper air observations and numerical model derived winds

Validation of Kalpana-1 atmospheric motion vectors (AMVs) against upper air radiosonde (RS) winds and numerical model-derived winds (National Centre for Medium Range Weather Forecasting's (NCMRWF's) T382L64 first guess) during the monsoon season of 2011 was attempted in this study. This was the first attempt to compare Kalpana-1 AMVs with model-derived winds. An AMV validation against RS winds showed that the mean AMV speed is always higher than that of the mean RS speed, except in high-level cloud motion vectors (CMVs). In the southwest monsoon season of 2011, the maximum speed bias in Kalpana-1 AMV with respect to RS winds was observed in the middle level (～5 m s^?1). The root mean square vector difference (RMSVD) of Kalpana-1 AMV with respect to the collocated RS winds (～5–7 m s^?1) has been found to be in the same range as those of other geostationary satellites, especially over the northern hemisphere and the tropics. The validation of Kalpana-1 AMVs against first guess revealed more erroneous low-level and middle-level AMVs, but the vector difference in the high-level winds was found to be smaller than the same in the low- and middle-level winds. The uncertainty in the empirical genetic algorithm (GA) used to derive the Kalpana-1 AMVs, which does not use model background fields, may be the reason for the high RMSVD of Kalpana-1 AMVs with respect to RS winds and high bias with respect to first guess. The mean observed AMV clearly depicted monsoonal features such as low-level westerly jet (LLWJ) and tropical easterly jet (TEJ). The speed bias density plots of Kalpana-1 high-level CMVs (400–100 hPa) and water vapour channel winds (WVWs) (above ～500 hPa) with respect to first guess showed that the bias was higher for WVWs; however, the standard deviations of high-level CMVs and WVWs are comparable.     

SAR-derived wind fields at the coastal region in the East/Japan Sea and relation to coastal upwelling

The relationship between the modification of synthetic aperture radar (SAR) wind field and coastal upwelling was investigated using high-resolution wind fields from Advanced Land Observing Satellite (ALOS) Phased Array type L-band synthetic aperture radar (PALSAR) imagery and sea-surface temperature (SST) from National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) data. The retrieved SAR wind speeds seem to agree well with in situ buoy measurements with only a relatively small error of 0.7 m s^?1. The SAR wind fields retrieved from the east coast of Korea in August 2007 revealed a spatial distinction between near and offshore regions. Low wind speeds of less than 3 m s^?1 were associated with cold water regions with dominant coastal upwelling. Time series of in situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. The low wind field from SAR was mainly induced by changes in atmospheric stability due to air–sea temperature differences. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature (R² = 0.63). The dependence of viscosity of water on radar backscattering at the present upwelling region was negligible since SAR data showed a relatively large backscattering attenuation to an SST ratio of 1.2 dB °C^?1. This study also addressed the important role of coastal upwelling on biological bloom under oligotrophic environments during summer.     

星载全极化微波辐射计海面风向反演仿真研究

全极化微波辐射计是一种可以测量海面辐射全部4个Stokes参数的新型被动遥感仪器,是测量海面风场、尤其是海面风向的新手段。介绍了国际首台全极化微波辐射计WindSat的海面风向信号谐波特征,在分析风向反演180°模糊度现象的成因基础之上,提出了利用WindSat能够进行前-后向双视扫描的优势去除风向反演180°模糊度问题的思路;通过构建海面风场仿真场景,在WindSat模拟亮温数据中加入高斯白噪声后,使用最大似然估计法和中值滤波技术开展了海面风向反演。比较和分析了使用WindSat前向刈幅和WindSat前-后向刈幅观测数据的风向反演结果。结果表明:由于能够有效去除风向反演180°模糊度,使用前-后向刈幅观测数据的风向反演精度要明显优于仅仅使用前向刈幅的反演结果。     

Seasonal behaviour of NCEP-NCAR longwave cloud radiative forcing and its relationship with all-India summer monsoon rainfall

Using National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) longwave cloud radiative forcing (LWCRF) reanalysis at the top of the atmosphere (TOA) for the period 1949–2006, the seasonal behaviour of the LWCRF and its relationship with the all-India summer monsoon rainfall (AISMR) during the winter (December–January–February, DJF), pre-monsoon (March–April–May, MAM) and summer monsoon (June– July–August–September, JJAS) seasons has been examined. The LWCRF over the Bay of Bengal region (15–20° N and 87.5–92.5° E) during the pre-monsoon season (MAM) is found to be significantly related to AISMR. The correlation coefficient (CC) between AISMR and LWCRF is 0.419, significant at the 1% level. The composite anomalies for excess minus deficient rainfall years of the LWCRF during MAM over the same region strongly support a significant relationship between LWCRF and AISMR. Thus, the LWCRF over the Bay of Bengal region in the pre-monsoon season appears to be a good indicator of the forthcoming monsoon rainfall.     

An alternative method for in-flight absolute radiometric calibration of thermal infrared channels of Chinese geostationary meteorological satellites

The aim of this study was to explore the feasibility of an alternative method for in-flight absolute radiometric calibration of the thermal infrared (TIR) channels of the Chinese meteorological satellites FengYun-2B (FY-2B) and FengYun-2C (FY-2C). The alternative method substituted radiosonde atmospheric profiles with those from the National Centers for Environmental Prediction (NCEP) reanalysis and the water surface brightness temperatures from TIR radiometers (CE312) with those from an automated hydrometeorological buoy (AHMB) system over Qinghai Lake (QHL), China. These data were then used to calculate the calibration coefficients and their uncertainty for the TIR channels of FY-2B and FY-2C. The at-sensor radiance (ASR) and at-sensor brightness temperature (ASBT) of the TIR channels of FY-2B and FY-2C were calculated by using 14 atmospheric profiles as measured by radiosonde over QHL in August 2003 and the corresponding NCEP reanalysis data, respectively. In addition, we conducted sensitivity tests to different atmospheric profiles of varying relative humidity and air temperatures on the ASR and ASBT of the TIR channels of FY-2B and FY-2C. Differences in gains between the regular and alternative methods are less than 0.005 mW m^–2 sr^?1 cm^?1 DN^?1. The sensitivity tests show that the ASR and ASBT are more sensitive to the relative humidity than the temperature in the atmospheric profile. Our results show that the proposed alternative method, of which the uncertainty is about 1.5 K for the TIR channels of FY-2B and FY-2C, is feasible for the TIR channels of various remote sensors. One of the major benefits of this alternative method is the potential for more frequent, reliable and inexpensive calibrations of the TIR sensors in operational conditions.     

Variations of surface boundary layer parameters associated with Cyclone Gonu over the Arabian Sea using QuikSCAT data

This study attempted to quantify the variations of the surface marine atmospheric boundary layer (MABL) parameters associated with the tropical Cyclone Gonu formed over the Arabian Sea during 30 May–7 June 2007 (just after the monsoon onset). These characteristics were evaluated in terms of surface wind, drag coefficient, wind stress, horizontal divergence, and frictional velocity using 0.5° × 0.5° resolution Quick Scatterometer (QuikSCAT) wind products. The variation of these different surface boundary layer parameters was studied for three defined cyclone life stages: prior to the formation, during, and after the cyclone passage. Drastic variations of the MABL parameters during the passage of the cyclone were observed. The wind strength increased from 12 to 22 m s^?1 in association with different stages of Gonu. Frictional velocity increased from a value of 0.1–0.6 m s^?1 during the formative stage of the system to a high value of 0.3–1.4 m s^?1 during the mature stage. Drag coefficient varied from 1.5 × 10^?3 to 2.5 × 10^?3 during the occurrence of Gonu. Wind stress values varied from 0.4 to 1.1 N m^?2. Wind stress curl values varied from 10 × 10^?7 to 45 × 10^?7 N m^?3. Generally, convergent winds prevailed with the numerical value of divergence varying from 0 to –4 × 10^?5 s^?1. Maximum variations of the wind parameters were found in the wall cloud region of the cyclone. The parameters returned to normally observed values in 1–3 days after the cyclone passage.     

The impact of assimilation of AMSU data for the prediction of a tropical cyclone over India using a mesoscale model

Tropical cyclones form over the seas: a typical data‐sparse region for conventional observations. Therefore, satellites, especially with microwave sensors, are ideal for cyclone studies. The advanced microwave sounding unit (AMSU) , in addition to providing very valuable data over non‐precipitating cloudy regions, can provide very high horizontal resolution of the temperature and humidity soundings. Such high‐resolution microwave data can improve the poorly analysed cyclone. The objective of this study is to investigate the impact of ingesting and assimilating the AMSU data together with conventional upper air and surface meteorological observations over India on the prediction of a tropical cyclone which formed over the Arabian Sea during November 2003 using analysis nudging. The impact of assimilating the AMSU‐derived temperature and humidity vertical profiles in a mesoscale model has not been tested yet over the Indian region. Such studies are important as most weather systems over India form over the seas. The present study is unique in the sense that it addresses the impact of ingesting and assimilating microwave sounding data (together with conventional India Meteorological Department data) on the prediction of a tropical cyclone, which formed over the Arabian Sea during November 2003 using analysis nudging. Two sets of numerical experiments are designed in this study. While the first set utilizes the National Center for Environmental Prediction (NCEP) reanalysis (for the initial and lateral boundary conditions) only in the fifth‐generation mesoscale model simulation, the second set utilized the AMSU satellite and conventional meteorological upper air and surface data to provide an improved analysis through analysis nudging. The results of the two sets of model simulations are compared with one another as well as with the NCEP reanalysis and the observations.

The results of the study indicated that the impact of ingesting and assimilating microwave sounding data and the conventional meteorological data through nudging resulted in an improvement in the simulation of wind asymmetries and the warm temperature anomalies. The with‐assimilation run simulated stronger wind speeds and stronger vertical velocity motion as compared with the without‐assimilation run. The time series of the minimum sea level pressure (SLP) and maximum wind speed for the simulations with the microwave sounding data and conventional meteorological data show better agreement with the observations than the simulations without the assimilation. The central minimum pressure of the simulations with the modified analysis are lower by 7 hPa as compared with the simulations without the assimilations. Even though there is not much of a difference in the maximum wind speed between the two simulations at the initial forecast time, the results indicate that the simulations with microwave sounding data and conventional meteorological data reveal a marked (9 m/s) increase in the maximum wind speed over the simulations without the assimilation. While the lowest central pressure estimated from the satellite image is 988 hPa, the simulations with microwave sounding data and conventional meteorological data show a value of 999.5 hPa for the lowest central minimum pressure. One reason for the inability of the simulation with improved analysis to achieve the observed lowest SLP is that the NCEP reanalysis had manifested an extremely weak system in the first place and, despite assimilation with microwave sounding data and conventional meteorological data, only a moderate improvement in the lowest SLP could be achieved. A proper appreciation of the impact of the microwave sounding data can be obtained by comparing with the lowest SLP obtained from the simulation without assimilation which showed a value of 1007 hPa. The initial mis‐representation in the location of the centre of the cyclone in the NCEP reanalysis with respect to the observed location has led to marked errors in the track prediction of both the model simulations. The assimilation of microwave satellite data is yet to be implemented in the current operational regional model over India and hence the results of this study may be relevant to the operational tropical cyclone forecasting community.     

Gridded surface wind fields from Metop/ASCAT measurements

This article deals with the calculation and validation of daily surface wind vector fields from wind speed and direction observations derived from Advanced SCATterometer (ASCAT) scatterometer measurements over the global ocean. According to the ASCAT sampling scheme, the objective method allowing for the determination of regular in space and time wind speed and direction fields uses ASCAT observations as well as European Centre for Medium Weather Forecasts (ECMWF) analyses. The latter are considered as external drift for the kriging method and as the temporal interpolation basis for ASCAT retrievals. This study focuses on the investigation of the capability of the method to add valuable wind information to the operational atmospheric analyses and on the quality of the resulting wind fields. The accuracy of the former is determined through comprehensive comparisons with daily winds calculated from moored buoy data. At global and regional scales, comparisons are performed with surface wind patterns derived from the ECMWF analysis and from ECMWF Re-analysis project (ERA-Interim) re-analyses.     

High radar-backscatter regions on Antarctic sea-ice and their relation to sea-ice and snow properties and meteorological conditions

The temporal and spatial variability of sea-ice radar signatures in the Southern Ocean during late winter, spring and early summer from QuikSCAT data is presented. We observe a circumpolar and broad band of sea-ice close to the marginal ice zone that is characterized by very high radar backscatter. This feature is explained through detailed in situ observations of snow and sea-ice properties as well as in relation to meteorological conditions, which were derived from US National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data. Our results indicate that high backscatter regions are caused by metamorphous snow, which forms through re-freezing after short-term melt events. This process is connected with the episodic passes of low-pressure systems entraining warmer air from the north. South of the Antarctic Circumpolar Trough, sea-ice is not affected by this influence and shows spatially homogenous microwave signatures with low backscatter.