S. Indira Rani

Intercomparison of Oceansat-2 and ASCAT Winds with In Situ Buoy Observations and Short-Term Numerical Forecasts

Abstract Sea surface winds from the Oceansat-2 scatterometer (OSCAT) are important inputs to Numerical Weather Prediction (NWP) models. The Indian Space Research Organization (ISRO) recently updated the OSCAT retrieval algorithm in order to generate better products. An attempt has been made in this study to evaluate the updated OSCAT winds using buoy observations and the 6-hour short-term forecasts from the T574L64 model from the National Centre for Medium Range Weather Forecasting (NCMRWF) during the 2011 monsoon. The results of the OSCAT evaluation are also compared with those from the Advanced Scatterometer (ASCAT) on-board the Meteorological Operational Satellite-A (MetOp-A) which were evaluated in the same way. The root mean square differences (RMSDs) for wind speed and direction, are within 2 m s−1 and 20° for both scatterometers. The RMSDs for OSCAT are slightly higher than those for ASCAT, and this difference may be attributed in part to the difference in frequency and resolution of the scatterometer payloads. The bias and standard deviation for ASCAT winds are also lower than those for OSCAT winds with respect to the model short-range forecast, and this can be attributed to the regular assimilation of ASCAT winds in the model.

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 Forecastings (NCMRWFs) 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.

Effect of new radiance observations on numerical weather prediction models

The assimilation of any new observational dataset into a numerical weather prediction (NWP) system can affect the quality of the existing datasets, with respect to the model background (the short-term forecast). This, in turn, influences the use of the existing observations within the NWP system. Indeed, it is the standard practice of operational NWP centers to assess the quality of observations with respect to NWP model fields. Furthermore, the importance of using NWP fields to assess the data quality from microwave sensing instruments has already been shown.1–3 The influence of a new dataset—from the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) instrument—on existing NWP models therefore needs to be assessed. The SAPHIR instrument is a six-channel microwave humidity profiler on the Megha-Tropiques (MT) satellite. The six channels are close to the absorption band of water vapor (at about 183GHz) and thus provide a relatively narrow weighting function, from the surface to an altitude of 10km, for retrieving water vapor profiles in the cloud-free troposphere. The new radiance/brightness temperatures (TBs) from SAPHIR have recently been added to the UK Met Office’s Unified Model (UM) assimilation system, which is being used in operations at India’s National Centre for Medium Range Weather Forecasting (NCMRWF). In this work,4 we have performed a detailed investigation of the impact of incorporating SAPHIR radiance data into the UK Met Office’s UM (i.e., which is used for NWPs). This UM Figure 1. Innovations (differences between the observations and simulations) for the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) channel 1. Results are shown with (blue curve) and without (black curve) bias correction.

Assimilation of CrIS Hyperspectral Radiances in a 4D-Var Assimilation System

This study demonstrates the advantage of the assimilation of Cross-track Infrared Sounder (CrIS) radiances of the Suomi-NPP satellite observation using 4D-Var assimilation system with global NCMRWF Unified Model (NCUM). The observation pre-processing system, quality control and thinning strategy for CrIS observations in addition to the impact of this observation in the analysis also discussed. Observation bias statistics are computed against the NCUM model fields from a short-range forecast (background) for quality control. The impact on forecasts is evaluated using “Observing System Simulation Experiments (OSSEs)”. The combined effect of hyperspectral and microwave radicalizes. The results show that CrIS data reduces the total number of observations and increases the RMS values for hyperspectral radiances.

Impact of AIRS radiance in the NCUM 4D-VAR assimilation system

The hyperspectral radiances from Atmospheric InfraRed Sounder (AIRS), on board NASA-AQUA satellite, have been processed through the Observation Processing System (OPS) and assimilated in the Variational Assimilation (VAR) System of NCMRWF Unified Model (NCUM). Numerical experiments are conducted in order to study the impact of the AIRS radiance in the NCUM analysis and forecast system. NCMRWF receives AIRS radiance from EUMETCAST through MOSDAC. AIRS is a grating spectrometer having 2378 channels covering the thermal infrared spectrum between 3 and 15 μm. Out of 2378 channels, 324 channels are selected for assimilation according to the peaking of weighting function and meteorological importance. According to the surface type and day-night conditions, some of the channels are not assimilated in the VAR. Observation Simulation Experiments (OSEs) are conducted for a period of 15 days to see the impact of AIRS radiances in NCUM. Statistical parameters like bias and RMSE are calculated to see the real impact of AIRS radiances in the assimilation system. Assimilation of AIRS in the NCUM system reduced the bias and RMSE in the radiances from instruments onboard other satellites. The impact of AIRS is clearly seen in the hyperspectral radiances like IASI and CrIS and also in infrared (HIRS) and microwave (AMSU, ATMS, etc.) sensors.

Assimilation of SAPHIR radiance: impact on hyperspectral radiances in 4D-VAR

Assimilation of a new observation dataset in an NWP system may affect the quality of an existing observation data set against the model background (short forecast), which in-turn influence the use of an existing observation in the NWP system. Effect of the use of one data set on the use of another data set can be quantified as positive, negative or neutral. Impact of the addition of new dataset is defined as positive if the number of assimilated observations of an existing type of observation increases, and bias and standard deviation decreases compared to the control (without the new dataset) experiment. Recently a new dataset, Megha Tropiques SAPHIR radiances, which provides atmospheric humidity information, is added in the Unified Model 4D-VAR assimilation system. In this paper we discuss the impact of SAPHIR on the assimilation of hyper-spectral radiances like AIRS, IASI and CrIS. Though SAPHIR is a Microwave instrument, its impact can be clearly seen in the use of hyper-spectral radiances in the 4D-VAR data assimilation systems in addition to other Microwave and InfraRed observation. SAPHIR assimilation decreased the standard deviation of the spectral channels of wave number from 650 -1600 cm-1 in all the three hyperspectral radiances. Similar impact on the hyperspectral radiances can be seen due to the assimilation of other Microwave radiances like from AMSR2 and SSMIS Imager.

IASI hyperspectral radiances in the NCMRWF 4D-VAR assimilation system: OSE

Accuracy of global NWP depends more on the contribution of satellite data than the surface based observations. This is achieved through the better usage of satellite data within the data assimilation system. Efforts are going on at NCMRWF to add more and more satellite data in the assimilation system both from Indian and international satellites in geostationary and polar orbits. Impact of the new dataset is assessed through Observation System Experiments (OSEs), through which the impact of the data is evaluated comparing the forecast output with that of a control run. This paper discusses one such OSEs with Infrared Atmospheric Sounder Interferometer (IASI) onboard MetOp-A and B. IASI is the main payload instrument for the purpose of supporting NWP. IASI provides information on the vertical structure of the atmospheric temperature and humidity with an accuracy of 1K and a vertical resolution of 1 km, which is necessary to improve NWP. IASI measures the radiance emitted from the Earth in 8641 channels, covering the spectral interval 645-2760 cm-1. The high volume data resulting from IASI presents many challenges, particularly in the area of assimilation. Out of these 8641 channels, 314 channels are selected depending on the relevance of information in each channel to assimilate in the NCMRWF 4D-VAR assimilation system. Studies show that the use of IASI data in NWP accounts for 40% of the impact of all satellite observations in the NWP forecasts, especially microwave and hyperspectral infrared sounding techniques are found to give the largest impacts

Comparison of INSAT-3D AOD over Indian region with satellite- and ground-based measurements: a data assimilation perspective

This paper aims at comparing the INSAT-3D AOD with other space based observations over the continental regions. INSAT-3D launched in 2013 is an advanced geostationary weather satellite of India at 82° East longitude provides Aerosol Optical Depth (AOD) observations at 650 nm over both land and ocean. The level-3 daily AOD measurements from MODIS (both Aqua and Terra) and MISR are used for comparison with that from INSAT-3D. This work is applied during premonsoon season of 2015. Overall statistical scores and systematic errors are compared to characterize various error sources. Our study indicates that significant differences exist between different aerosol observations which may be partly due to retrieval algorithm, sensor configurations and temporal sampling. Comparison of INSAT observed AOD shows less bias towards MISR and MODIS-Terra observed AOD than with MODIS-Aqua. The INSAT observations over oceanic region have better correlation, minimum bias and rmse than land region. Overall, the mean bias of the dataset is ±0.05, with a root mean square error of 0.22, but these errors are also found highly dependent on geographical region. Additionally, we compared INSAT 660 nm AOD with two AERONET ground stations. The comparison of INSAT with different observations shows that the retrieved AOD is closer to the ground-based data than the MISR and MODIS AOD.

A review of the space based remote sensing for NWP

Space based remote sensing provides continuous and contiguous information about the state of the Earth-atmosphere system which is crucial to Numerical Weather Prediction (NWP). Since 1960, after the successful launch of the first weather satellite TIROS-1, a range of weather satellites carrying different sensors to monitor atmospheric parameters used in NWP have not only improved the weather forecasting but also enhanced our understanding of the physical and dynamical processes in the atmosphere. Satellite based earth observing system provides data in different spatial and temporal resolutions from the geostationary and low-earth orbits. This review briefly describes general introduction to both active and passive satellite remote sensing, various satellite sensors used for NWP applications in the past an d in the present and observational data requirements for future NWP models. The presentation also includes the importance of re-calibration of satellite observations of the past, especially the data from Indian satellites (INSAT series) which can be used in the atmospheric reanalysis in the future.

Use of INSAT-3D sounder and imager radiances in the 4D-VAR data assimilation system and its implications in the analyses and forecasts

INSAT-3D, the first Indian geostationary satellite with sounding capability, provides valuable information over India and the surrounding oceanic regions which are pivotal to Numerical Weather Prediction. In collaboration with UK Met Office, NCMRWF developed the assimilation capability of INSAT-3D Clear Sky Brightness Temperature (CSBT), both from the sounder and imager, in the 4D-Var assimilation system being used at NCMRWF. Out of the 18 sounder channels, radiances from 9 channels are selected for assimilation depending on relevance of the information in each channel. The first three high peaking channels, the CO2 absorption channels and the three water vapor channels (channel no. 10, 11, and 12) are assimilated both over land and Ocean, whereas the window channels (channel no. 6, 7, and 8) are assimilated only over the Ocean. Measured satellite radiances are compared with that from short range forecasts to monitor the data quality. This is based on the assumption that the observed satellite radiances are free from calibration errors and the short range forecast provided by NWP model is free from systematic errors. Innovations (Observation – Forecast) before and after the bias correction are indicative of how well the bias correction works. Since the biases vary with air-masses, time, scan angle and also due to instrument degradation, an accurate bias correction algorithm for the assimilation of INSAT-3D sounder radiance is important. This paper discusses the bias correction methods and other quality controls used for the selected INSAT-3D sounder channels and the impact of bias corrected radiance in the data assimilation system particularly over India and surrounding oceanic regions.