This study demonstrates the data assimilation perspective of vertical profiles of winds derived from the Doppler weather radar (DWR) data using the volume velocity processing (VVP) technique. The VVP information from the nine Indian DWR stations is used in this study. The winds from the Indian DWR network are assessed for their quality based on the National Center for Medium Range Weather Forecasting unified model (NCUM). This paper describes the quality of DWR VVP winds, preprocessing of VVP wind data, and their use in NCUM 4 D-Var assimilation systems. The VVP winds are compared against an NCUM background (short forecast) to understand the observation bias. Comparison of VVP winds is also made with colocated radiosonde wind observations. The VVP winds show less bias when compared against model background especially in the region of strong wind flow. The correlation between the observations and the model background is greater than 0.7 for most of the radars. The VVP winds provide reasonably accurate estimates of the vertical wind structure in the troposphere over radar locations, which can be effectively used in the numerical weather prediction system.
Doppler Weather Radar (DWR) can provide tropospheric wind observations with high temporal and spatial resolutions. The Volume Velocity Processing (VVP) technique is one of the processing methods which can provide vertical profiles of mean horizontal winds. The DWR observed VVP winds gives a continuous observation of the wind field at various atmospheric levels. The quality of the VVP winds is studied against the short-range forecast of the NCUM model (model background). The biases of the observation are calculated against model background. This study focuses on the quality of VVP winds and seasonal variation of bias of the observed wind. This results shows that the VVP winds provides reasonably accurate estimates of the vertical wind structure in the troposphere over radar locations which can be effectively used in the numerical weather prediction system.
Tropical cyclones (TCs) have strong impact on socio-economic conditions of the countries like India, Bangladesh and Myanmar owing to its awful devastating power. This brings in the need of precise forecasting system to predict the tracks and intensities of TCs accurately well in advance. However, it has been a great challenge for major operational meteorological centers over the years. Genesis of TCs over data sparse warm Tropical Ocean adds more difficulty to this. Weak and misplaced vortices at initial time are one of the prime sources of track and intensity errors in the Numerical Weather Prediction (NWP) models. Many previous studies have reported the forecast skill of track and intensity of TC improved due to the assimilation of satellite data along with vortex initialization (VI). Keeping this in mind, an attempt has been made to investigate the impact of vortex initialization for simulation of TC using UK-Met office global model, operational at NCMRWF (NCUM). This assessment is carried out by taking the case of a extremely severe cyclonic storm "Chapala" that occurred over Arabian Sea (AS) from 28th October to 3rd November 2015. Two numerical experiments viz. Vort-GTS (Assimilation of GTS observations with VI) and Vort-RAD (Same as Vort-GTS with assimilation of satellite data) are carried out. This vortex initialization study in NCUM model is first of its type over North Indian Ocean (NIO). The model simulation of TC is carried out with five different initial conditions through 24 hour cycles for both the experiments. The results indicate that the vortex initialization with assimilation of satellite data has a positive impact on the track and intensity forecast, landfall time and position error of the TCs.
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.
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
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.
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