A Technique of Rainfall Assimilation for Dynamic Extended Range Monsoon Prediction (original) (raw)
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Rainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four-dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.
Advances in Space Research, 2010
A number of experiments were conducted to study the impact of updating model basic fields by satellite data (Quick Scatterometer (QSCAT) surface winds and Atmospheric Infrared Sounder (AIRS) temperature and humidity profiles) on long-range simulation during the Indian summer monsoon 2006. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model version5 (MM5) and its four dimensional data assimilation (FDDA) technique was used for the numerical simulations. The spatial distribution and temporal variation in model simulated basic meteorological parameters and rainfall were verified against the observed fields from National Center for Environmental Prediction (NCEP) analysis and Tropical Rainfall Measuring Mission (TRMM), respectively. The overall analysis of the results from QSCAT surface wind assimilation as compared to control simulation (CNT; without the satellite data assimilation) suggest that a better representation of a single level wind field during model integration fail to make significant improvement in the model simulation both in the basic meteorological parameters and rainfall. The assimilation of temperature and humidity profiles from the AIRS during model integration significantly improved the rainfall prediction during monsoon period. It is found that the improvement in rainfall prediction is attributed to improved thermodynamics structure due to AIRS profile assimilation and the degree of improvement is more in temperature prediction as compared to humidity prediction. It is also found that the prediction over the regions, such as south west part of India and foothills of Himalaya, where a complex orography exists, is not significantly benefited from satellite data assimilation which highlights the need of improvement in the model in addition to a better representation of atmospheric state.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
This study estimates the improvement in the simulation of Indian summer monsoon (ISM) circulation in the weather research and forecasting (WRF) model by assimilating temperature profiles from atmospheric infrared sounder. Two experiments are carried out from 1st May to 1st October during 2003-2011. In the first experiment control (CTRL), National Centers for Environmental Prediction final analysis forcing is used; whereas the second one (WRFAIRS) is same as CTRL but temperature profiles are assimilated. The improvements in the simulation are quantified using different statistical scores. Overall, the assimilation has improved the spatial and temporal distribution of various fields associated with ISM. Some of the major improvements are 1) elimination of asymmetric (north-south) SLP bias; 2) larger error reduction in winds; 3) reduction in the temperature biases at boundary layer and midtroposphere; 4) improvement in the vertical wind shear; 5) reduction in the water vapor mixing ratio errors by 0.3-0.6 g • Kg −1 ; and 6) improved simulation of monsoon circulation indices. Further improvements are noticed in dynamic and thermodynamic fields over different convective regions. This study advocates that accurate representation of the thermal structure in WRF is crucial for the simulation of realistic monsoon circulation. It may further pave way for developing/improving convective parameterization schemes for the model.
Frontiers in climate, 2022
This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematically rigorous technique that has been recently introduced to constrain the large-scale features of high-resolution atmospheric models with coarse spatial scale data. It is similar to spectral nudging but does not require any spectral decomposition for scales separation. This is expected to be particularly relevant for ISM, which involves various interactions between large-scale circulations and regional physical processes. Along with a control simulation, several downscaling simulations were conducted with the Weather Research and Forecasting (WRF) model configured over the Indian monsoon region at 10-km horizontal resolution using CDA, spectral (retaining different wavenumbers) and grid nudging for three contrasting ISM rainfall seasons: normal (2016), excess (2013), and drought (2009). The simulations are nested within the global NCEP Final Analysis data available at 1 º x 1 º horizontal resolution. The model outputs are evaluated against the India Meteorological Department (IMD) gridded precipitation and the fifth generation ECMWF atmospheric reanalysis (ERA-5). Compared to grid and spectral nudging, the simulations using CDA produce enhanced ISM features over the Indian subcontinent including the low-level jet, tropical easterly jet, easterly wind shear, and rainfall distributions for all investigated ISM seasons. The major ISM processes, in particular the monsoon inversion over the Arabian Sea, tropospheric temperature gradients and moist static energy over central India, and zonal wind shear over the monsoon region, are all better simulated with CDA. Spectral nudging outputs are found to be sensitive to the choice of the wavenumber, requiring careful tuning to provide robust simulations of the ISM system. In contrast, control and grid nudging generally fail to well reproduce some of the main ISM features.
Remote Sensing Letters, 2017
This study reports the improvement in the predictability of circulation and precipitation associated with monsoon intraseasonal oscillations (MISO) when the initial state is produced by assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and water vapour profiles in Weather Research Forecast (WRF) model. Two separate simulations are carried out for nine years (2003 to 2011). In the first simulation, forcing is from National Centers for Environmental Prediction (NCEP, CTRL) and in the second, apart from NCEP forcing, AIRS temperature and moisture profiles are assimilated (ASSIM). Ten active and break cases are identified from each simulation. Three dimensional temperature states of identified active and break cases are perturbed using twin perturbation method and carried out predictability tests. Analysis reveals that the limit of predictability of low level zonal wind is improved by four (three) days during active (break) phase. Similarly the predictability of upper level zonal wind (precipitation) is enhanced by four (two) and two (four) days respectively during active and break phases. This suggests that the initial state using AIRS observations could enhance predictability limit of MISOs in WRF. More realistic baroclinic response and better representation of vertical state of atmosphere associated with monsoon enhance the predictability of circulation and rainfall.
Journal of Earth System Science, 2007
Objective analysis of daily rainfall at the resolution of 1 o grid for the Indian monsoon region has been carried out merging dense land rainfall observations and INSAT derived precipitation estimates. The daily analysis, being based on significant high dense rain gauge observations, is found to be very realistic to resolve detailed features of Indian summer monsoon. The new analysis could distinctly capture characteristic features of summer monsoon such as, north-south oriented belt of heavy rainfall along the western Ghats with sharp gradient of rainfall between the west coast heavy rain region and the rain shadow region to the east, pockets of heavy rainfall along the location of monsoon trough/low over the east central parts of country, over northeast India, along the foot hills of Himalayas and over the north Bay of Bengal. When this product is used to asses the quality of other available standard climate products (CMAP and ECMWF reanalysis) at the grid resolution of 2.5 o , it is found that orographic heavy rainfall along western Ghats of India is poorly resolved by them. However, the GPCC analysis (gauge only) at the resolution of 1 o grid closely discerns the new analysis. The case studies illustrated show that the daily analysis is able to capture large scale as well as mesoscale features of monsoon precipitation systems. The study with two seasons (2001 and 2003) data has shown sufficiently promising results for operational application, particularly for the validation of NWP models.
Assimilation and forecast experiments have been carried out in this study using conventional observations as well as total precipitable water and surface wind data retrieved from the Special Sensor for Microwave Imaginary (SSM/I) sensors. The main objectives of this study were to document the bias in short-range predictions of the Weather Research and Forecasting (WRF) version 3.1 model over the Indian region during the summer monsoon season and the impact of SSM/I data. All the experiments were carried out in the monsoon seasons of 2001 as a part of pilot phase studies for the South Asian Regional Reanalysis (SARR) project. It is seen that the model has strong bias in wind forecasts over the Arabian Sea and the Indian Ocean. A cyclonic bias in the forecasts exists over south-west India. Over the equatorial Indian Ocean, a strong southerly bias towards the Bay of Bengal is noticed. The model has a systematic bias to increase moisture over most parts of the equatorial Indian Ocean. Except over the Gangetic plains, the model exhibits dry bias with reduced moisture over most parts of India in 24 hour forecasts. The impact of assimilation of SSM/I products has been to increase the moisture over the Bay of Bengal, where the model has shown dry bias. The moisture content over the equatorial Indian Ocean (western sector) reduced significantly after assimilation of SSM/I data, where the model has a tendency to enhance moisture. Major rainfall zones during the monsoon season are brought out well in 6 hour forecasts by the model; however, the rainfall amount increased over the Bay of Bengal due to the assimilation of SSM/I data. These features are consistent with the moisture and wind differences between the two assimilation experiments. A quantitative verification of model rainfall in terms of equitable threat scores indicate that the accuracy of rainfall products is higher when SSM/I data are assimilated. It is seen that the general pattern of rainfall tendency in 24 hour forecasts remains the same irrespective of whether the forecast initial conditions are with or without SSM/I data. Examination of a case of monsoon depression showed that assimilation of SSM/I data improved the analysis.
Meteorology and Atmospheric Physics, 2008
The Fifth Generation Mesoscale Model (MM5) is used to study the effect of assimilated satellite and conventional data on the prediction of three monsoon depressions over India using analysis nudging. The satellite data comprised the vertical profiles of temperature and humidity (NOAA-TOVS: -National Oceanic and Atmospheric Administration-TIROS Operational Vertical Sounder; MODIS: -MODerate resolution Imaging Spectroradiometer) and the surface wind vector over the sea (QuikSCAT: -Quick Scatterometer); the conventional meteorological data included the upper-air and surface data from the India Meteorological Department (IMD). Two sets of numerical experiments are performed for each case: the first set, NOFDDA (no nudging), utilizes NCEP reanalysis (for the initial conditions and lateral boundary conditions) in the simulation, the second set, FDDA, employs the satellite and conventional meteorological data for an improved analysis through analysis nudging. Two additional experiments are performed to study the effect of increased vertical and horizontal resolution as well as convective parameterization for one of the depressions for which special fields observations were available. The results from the simulation are compared with each other and with the analysis and obser-vations. The results show that the predicted sea level pressure (SLP), the lower tropospheric cyclonic circulation, and the precipitation of the FDDA simulation reproduced the large-scale structure of the depression as manifested in the NCEP reanalysis. The simulation of SLP using no assimilation high-resolution runs (HRSKF10KM, HRSKF3.3KM) with the Kain-Fritsch cumulus parameterization scheme appeared poor in comparison with the FDDA run, while the no assimilation high-resolution runs (HRSGR10KM, HRSGR3.3KM) with the Grell cumulus scheme provided better results. However, the space correlation and the root mean square (rms) error of SLP for the HRSKF10KM was better than the FDDA; the largest and smallest space correlation for HRSKF10KM, FDDA, and HRSGR10KM were 0.894 and 0.623, 0.663 and 0.195, and 0.733 and 0.338 respectively; the smallest and largest rms error for HRSKF10KM, FDDA and HRSGR10KM were 1.879 and 5.245, 2.308 and 4.242, and 2.055 and 4.909 respectively. The precipitation simulations with the 3.3 km high-resolution, no assimilation runs performed no better than the precipitation simulation with the FDDA run. Thus, a significant finding of this study is that over the Indian monsoon region, the improvements in the simulation using nudging in the FDDA run are of similar magnitude (or better) than the improvements in the simulation due to high-resolution and to cumulus parameterization sensitivity. The improvements in the FDDA run due to analysis nudging were also verified in two more depression cases. The current operational regional models in India do not incorporate the Correspondence: