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.
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.
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.
Warm core is the characteristic that distinguishes tropical cyclones from its extra tropical counter parts, where the center of the cyclone is warmer than its environment. Two of the most common variables used to characterize the warm core are its strength and height. The strength is given by the magnitude of maximum perturbation temperature and the height is the level where the maximum perturbation temperature occurs. INSAT-3D, India's advanced weather satellite, is the first geostationary sounder over India and the surrounding Oceanic regions. INSAT-3D has 18 channel sounder with a resolution of 10 km to profile the atmospheric temperature and humidity. Brightness Temperatures (Tbs) from INSAT-3D sounder channels are used to analyze the warm core structure of Tropical cyclone Phailin (8–14 October 2013) over the North Indian Ocean. Only when the system becomes very severe cyclonic system, when the eye of the cyclone is clearer (fully cloud free), the sounder channel Tbs showed multiple maxima, with strong primary maximum in the middle level (600–500 mb) and the secondary maximum in the upper level (300–250 mb), unlike the conventional belief suggested warm core existence at 250 mb. Due to the high resolution of (10 km) INSAT-3D sounder channels, compared to the Micro wave channels (AMSU-A of 50 km resolution), the warm core structure below 10 km of the atmosphere is well resolved.
The socioeconomic aspects of life in coastal regions of India are significantly affected by tropical cyclones (TCs) over North Indian Ocean (NIO). It is well known that the lack of conventional observation over the ocean is a critical factor limiting the accuracy of the TC forecast. The goal of this study is to assess the impact of hyperspectral sounder measurements from Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) in the MetOp satellite on TC simulation using NCMRWF Unified Model (NCUM) with 17 km horizontal resolution. The results of the study indicate that the assimilation of hyperspectral radiance data has a positive impact on the prediction of track and intensity of TC.
KEYWORDS: Satellites, Error analysis, Atmospheric modeling, Data modeling, Motion analysis, Medium wave, Systems modeling, Control systems, Backscatter, Infrared radiation
Sea surface wind vectors from Advanced Scatterometer (ASCAT) onboard MetOP satellites are pivotal inputs to NWP models, especially during cyclone period. NCMRWF regularly receives these winds from NOAA through ftp and routinely assimilates in the operational global models. The impact of ASCAT winds in the NCMRWF Unified Model (NCUM) assimilation and forecast system during the cyclone Chapala period from 28 October 2015 to 4 November 2015 is studied. Before assimilating, these winds are validated against in-situ observations from buoy platforms over the North Indian Ocean (NIO). It is found that the errors in the ASCAT winds are well within the limit of mission goal (<2m/s). After the successful validation, numerical experiments are designed in such a way that 10-day forecasts are generated from two different initial conditions. In the control run (CTL), ASCAT winds are removed from the Observation Processing System (OPS) and Variational Assimilation (VAR) systems of NCUM, while in the experiment run (AST) ASCAT winds are included in both OPS and VAR. Forecasts from both the runs are analysed to see the movement and intensification of the cyclonic system in due course. The results show that the experiments with ASCAT winds improved the track and intensity of the NIO cyclonic system.
This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances from the Suomi-NPP satellite in the NCMRWF Unified Model (NCUM). ATMS is a cross-track scanning microwave radiometer inherited the legacy of two very successful instrument namely, Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS). ATMS has 22 channels: 11 temperature sounding channels around 50-60 GHz oxygen band and 6 moisture sounding channels around the 183GHz water vapour band in addition to 5 channels sensitive to the surface in clear conditions, or to water vapour, rain, and cloud when conditions are not clear (at 23, 31, 50, 51 and 89 GHz). Before operational assimilation of any new observation by NWP centres it is standard practice to assess data quality with respect to NWP model background (short-forecast) fields. Quality of all channels is estimated against the model background and the biases are computed and compared against that from the similar observations. The impact of the ATMS data on global analyses and forecasts is tested by adding the ATMS data in the NCUM Observation Processing system (OPS) and 4D-Var variational assimilation (VAR) system. This paper also discusses the pre-operational numerical experiments conducted to assess the impact of ATMS radiances in the NCUM assimilation system. It is noted that the performance of ATMS is stable and it contributes to the performance of the model, complimenting observations from other instruments.
Atmospheric Motion Vector (AMV) over Indian Ocean and surrounding region is one of the most important sources of tropospheric wind information assimilated in numerical weather prediction (NWP) system. Earlier studies showed that the quality of Indian geo-stationary satellite Kalpana-1 AMVs was not comparable to that of other geostationary satellites over this region and hence not used in NWP system. Indian satellite INSAT-3D was successfully launched on July 26, 2013 with upgraded imaging system as compared to that of previous Indian satellite Kalpana-1. INSAT-3D has middle infrared band (3.80 - 4.00 μm) which is capable of night time pictures of low clouds and fog. Three consecutive images of 30-minutes interval are used to derive the AMVs. New height assignment scheme (using NWP first guess and replacing old empirical GA method) along with modified quality control scheme were implemented for deriving INSAT-3D AMVs. In this paper an attempt has been made to validate these AMVs against in-situ observations as well as against NCMRWF's NWP first guess for monsoon 2015. AMVs are subdivided into three different pressure levels in the vertical viz. low (1000 – 700 hPa), middle (700 – 400 hPa) and high (400 – 100 hPa) for validation purpose. Several statistics viz. normalized root mean square vector difference; biases etc. have been computed over different latitudinal belt. Result shows that the general mean monsoon circulations along with all the transient monsoon systems are well captured by INSAT-3D AMVs, as well as the error statistics viz., RMSE etc of INSAT-3D AMVs is now comparable to other geostationary satellites.
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
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.
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 (OSSE's)”. 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.
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.
In this article, we describe the variation of air-sea exchange coefficients and air-sea interface fluxes over the East Asian marginal seas surrounding the Korean peninsula and compare them with the similar estimates reported for the tropical Indian Ocean. Surface layer meteorological observations for a period of about five years obtained from five oceanic buoys in the adjoining seas of Korean peninsula form the database for this study. Depending on the stability of the atmosphere, buoy data is classified into three categories - unstable, neutral and stable data. For unstable conditions, sensible and latent heat flux show good correlation with the wind speed, whereas it is not so for the neutral and stable condition. Irrespective of the stability of the atmosphere, momentum flux always shows a steady dependence on the varying wind speed. Sensible and latent heat fluxes also show good correlation with the difference between sea surface temperature and air temperature. Unlike the linear regression between the exchange coefficients and wind speeds reported for the Indian Ocean, we suggest second order and exponential fits for these exchange coefficients, which give better representation of their wind speed dependence. The results presented in this article form very useful input to the coupled ocean atmospheric models and the oceanic wave models, hence significant.
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