The impact of background error parameters on heavy rainfall prediction

A heavy rainfall event over the northwest of India is selected to investigate the impact of Atmospheric Infrared Sounder (AIRS)-retrieved temperature and moisture profile assimilation on regional model prediction. The Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system (WRFDA) is used to assimilate AIRS profiles with tuning of two major background error parameters – viz. length and variance scales. Assimilation of AIRS profiles improves the WRF model analyses, which are closer to the Moderate Resolution Imaging Spectrometer (MODIS) profiles compared to those without assimilation experiment. Results show that within a wide parameter range of length and variance scales, the assimilation of AIRS-retrieved profiles has a positive influence on heavy rainfall prediction. Approximately 9–30, 5–42, and 0.5–3.0% domain average values of improvement are observed after AIRS profile assimilation for different values of length and variance scales in temperature, water vapour mixing ratio, and rainfall prediction, respectively. This study shows that the impact of observations on the WRF model forecast is dependent on the length and variance scale parameters of background error, and lower values of length scale in WRFDA result in degradation of the forecast.     

The impact of assimilation of MODIS data for the prediction of a tropical low‐pressure system over India using a mesoscale model

A low‐pressure system formed over the Bay of Bengal, India on 15 October 2003 and crossed the east coast of India during the late hours of 17 October 2003. The system, which provided copious rainfall over the Bay of Bengal and stations on the east coast, is investigated in this study using the fifth Generation Mesoscale Model (MM5). Three sets of numerical experiments are designed in this study. While the first set utilizes National Center for Environmental Prediction—Aviation (NCEP‐AVN) analysis (for the initial conditions and lateral boundary conditions) only in the MM5 simulation, the second set utilizes the vertical profiles of temperature and humidity obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) (as well as a few radiosonde station data) to provide an improved analysis. The third set used the vertical profiles of temperature and humidity from MODIS alone to provide an improved analysis. The results of the three sets of simulation are compared with one another as well as with the analysis and observations. It is found that the predicted sea level pressure of the MM5 simulation which utilized the improved analysis: reproduces the large‐scale structure of the low‐pressure system as manifested in the NCEP‐AVN analysis; provides a stronger and deeper low‐pressure system as seen from the sea level pressure field; and shows a larger northward extent of the associated precipitation pattern as compared with the simulation with just the analysis. The results of the third experiment (impact of vertical profiles of temperature and humidity using MODIS alone) compare well with the results of the second experiment except that in the former, the associated cyclonic circulation in the lower troposphere appears weaker. The results of this study, although restricted to a single case study, demonstrate that inclusion of MODIS derived vertical temperature and humidity profiles together with radiosonde data caused a favourable impact on the simulated structure of the low‐pressure system.     

Effects of implementing satellite observed aerosols into a mesoscale atmosphere model

This study investigated the performance of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model (MM5) in calculating the aerosol forcing on cloud cover, incoming surface solar radiation, and near-surface air temperature via the implementation of aerosol optical depth in the shortwave radiation parameterization. MM5 simulations with and without aerosol data are performed in the periods of 6–7 August 2003 and 19–21 September 2003 during which strong aerosol forcing was observed with Moderate Resolution Imaging Spectroradiometer (MODIS) data in the mid-Atlantic region. Both periods clearly showed that aerosols had a direct negative effect on surface solar radiation through aerosol scattering. For example, every 0.1 change in MODIS aerosol optical thickness (AOT) results in 44 and 59?W?m^?2 decreases in surface solar radiation for the first and second periods, respectively. A magnitude of 0.1 increment in MODIS AOT reduces air temperature 0.36 and 0.56?K for the first and second periods, respectively. Comparisons with satellite-derived surface solar radiation retrievals showed that aerosol implementation in MM5 consistently showed better incoming surface solar radiation than that of the non-aerosol case. This helps to reduce uncertainties related to the radiation–cloud–aerosol interaction in numerical weather modelling systems.     

Importance of satellite-retrieved rain/no-rain information on short-range weather predictions

The impact of assimilating rain (satellite-retrieved rainfall is greater than zero) and no-rain (satellite-retrieved rainfall is equal to zero) information retrieved from the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation is assessed during Indian summer monsoon 2013 using the weather research and forecasting (WRF) model. Daily three parallel experiments are performed with and without satellite rainfall assimilation for short-range weather forecasts. Additional two experiments are performed daily to evaluate the sensitivity of cumulus parameterization on the WRF model predictions when precipitations are used for assimilation. Precipitation assimilation improves the 48 h low-level temperature, moisture, and winds predictions. Rainfall prediction is also improved over central India when satellite-retrieved rainfall information are assimilated compared to without rainfall assimilation (CNT) experiments. More improvements are seen in moisture forecasts when the Kain–Fritsch (KF) cumulus convection parameterization scheme is used against the Grell–Devenyi ensemble (GD) scheme, whereas for temperature and wind speed forecasts the Grell convection parameterization scheme performed better over the Indian region. Overall, precipitation assimilation improved the WRF model analysis and subsequent model forecasts compared with without precipitation assimilation experiments. Results show that no-rain observations also have a significant positive impact on short-range weather forecasts.     

Evaluation and assimilation of various satellite-derived rainfall products over India

Accurate prediction of rainfall from the numerical weather prediction model is one of the major objectives over tropical regions. In this study, four different satellite-derived rainfall products (viz. merged-rainfall product from TRMM (Tropical Rainfall Measuring Mission) 3B42 and IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)), and Indian meteorological satellite INSAT-3D retrieved HEM (Hydro-Estimator Method) and IMSRA (INSAT Multi-Spectral Rainfall Algorithm) rainfall) are assimilated in the Weather Research and Forecasting (WRF) model using variational method. Before assimilation of satellite retrieved rainfall product in the WRF model, selected rainfall products are compared with ground rainfall from India Meteorological Department during Indian summer monsoon (June–September) 2015. Preliminary validation results show root-mean-square-difference (mean difference) of 18.1 (2.1), 21.3 (2.1), 15.4 (?0.72), and 14.4 (0.5) mm day^?1 in IMSRA, HEM, IMERG, and TRMM 3B42 rainfall, respectively. Further, the four-dimensional variational data assimilation method is used daily to assimilate selected rainfall products in the WRF model during the entire month of August 2015. Results suggest that assimilation of satellite rainfall improved the WRF model analyses and subsequent temperature and moisture forecasts. Moreover, rainfall prediction is also improved with the maximum positive impact from TRMM rainfall assimilation followed by IMERG rainfall assimilation. Similar nature of improvements is also seen in rainfall prediction when INSAT-3D retrieved rainfall products (HEM and IMSRA) are used for assimilation.     

Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter

Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m^− 2 at BTS and DGS and 2 W m^− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes.     

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.     

Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone

This study aims to investigate the impact of the Three-Dimensional Variational (3DVAR) assimilation of Doppler Weather Radar (DWR) wind data together with the India Meteorological Department (IMD) upper air and surface data for the prediction of a tropical cyclone, which formed over the Bay of Bengal. The National Centers for Environmental Prediction Final Analyses (NCEP FNL) data are used to produce initial conditions. Three numerical experiments were designed to study the effect of 3DVAR assimilation. For the first experiment, the model integrations were performed without any assimilation of observations. IMD upper air and surface observations were assimilated using 3DVAR for the second experiment and the third experiment assimilated DWR wind data along with IMD observations. The model results are compared with one another and also with the observations. The results of the study indicate that the assimilation of DWR wind data and IMD data have resulted in improvements in the simulation of strong vertical velocity, higher warm core temperature and strong gradients in the horizontal wind speed as well as improved spatial distribution of the precipitation.     

The effects of driver data on the performance of the FinROSE chemistry transport model

In this paper we tested the performance of the FinROSE chemistry transport model for three different datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Global middle atmospheric simulations from 1990 to 2005 were done using winds and temperatures from the ECMWF re-analysis (ERA) datasets, the ERA-40, the operational and the ERA-Interim. Analysis data was used in all simulations. The performance of the model for each dataset was analysed using the simulated stratospheric age-of-air and the ascent rate in the tropics. The ERA-40 data produced with a three-dimensional variational assimilation system (3D-Var) resulted in a too strong Brewer–Dobson circulation. The operational analysis produced with a four-dimensional variational assimilation system (4D-Var) gave somewhat improved results, and the new 4D-Var ERA-Interim re-analysis resulted in a much more realistic upward transport. Also the modelled ozone showed better agreement with observations when using the new re-analysis data.     

Four-dimensional variational data assimilation for high resolution nested models

Four-dimensional variational data assimilation (4D-Var) is used in environmental prediction to estimate the state of a system from measurements. When 4D-Var is applied in the context of high resolution nested models, problems may arise in the representation of spatial scales longer than the domain of the model. In this paper we study how well 4D-Var is able to estimate the whole range of spatial scales present in one-way nested models. Using a model of the one-dimensional advection–diffusion equation we show that small spatial scales that are observed can be captured by a 4D-Var assimilation, but that information in the larger scales may be degraded. We propose a modification to 4D-Var which allows a better representation of these larger scales.     

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.     

The impact of satellite-derived wind data assimilation on track,intensity and structure of tropical cyclones over the North Indian Ocean

In the present satellite era, remote-sensing data are more useful to improve the initial condition of the model and hence the forecast of tropical cyclones (TCs) when they are in the deep oceans, where conventional observations are unavailable. In this study, an attempt is made to assess the impact of remotely sensed satellite-derived winds on initialization and simulation of TCs over the North Indian Ocean (NIO). For this purpose, four TCs, namely, ‘Nargis’, ‘Gonu’, ‘Sidr’ and ‘KhaiMuk’, are considered, with 13 different initial conditions. Two sets of numerical experiments, with and without satellite-derived wind data assimilation, are conducted using a high-resolution weather research and forecasting (WRF) model.

The inclusion of satellite-derived winds through a three-dimensional variational (3DVAR) data assimilation system improves the initial position in 11 cases out of 13 by 34%. The 24-, 48-, 72- and 96-hour mean track forecast improves by 28%, 15%, 41% and 47%, respectively, based on 13 cases. The landfall prediction is significantly improved in 11 cases by about 37%. The intensity prediction also improves by 10–20%. Kinematic and thermodynamic structures of TCs are also better explained, as it could simulate heat and momentum exchange between sea surface and upper air. Due to better simulation of structure, intensity and track, the 24-hour accumulated rainfall intensity and distribution are also well predicted with the assimilation of satellite-derived winds.     

Spatiotemporal pattern validation of chlorophyll-a concentrations in Lake Okeechobee,Florida, using a comparative MODIS image mining approach

A comparative analysis was conducted using three types of data-mining models produced from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Surface Reflectance 1-day or 8-day composite images to estimate chlorophyll-a (chl-a) concentrations in Lake Okeechobee, Florida. To understand the pros and cons of these three models, a genetic programming (GP) model was compared to an artificial neural network (ANN) model and multiple linear regression (MLR) model with respect to two different data sets related to model formulation. The first data set included the MODIS Terra bands from 1 to 7; the second data set extended the first data set by adding environmental parameters such as Secchi disc depth (SDD), total suspended solids (TSS), wind speed, water level, rainfall and air temperature collected around the lake in 2003 and 2004. The GP algorithm, which has an advantage in machine learning allowing us to select the appropriate input parameters that significantly impact the prediction accuracy, outperformed the other two models based on four statistical indices. Specifically, the GP modelling outputs revealed interesting determinations of chl-a concentrations for MODIS bands 3, 5, 6 and 7, corresponding to wavelengths 459–479, 1230–1250, 1628–1652 and 2105–2155 nm, respectively. The number of training data points is limited; therefore, the inclusion of additional environmental variables cannot improve the prediction accuracy of the GP-derived chl-a concentrations.     

Variational optimization for global climate analysis on ESA's high performance computing grid

State of the arte assimilation techniques, such as 3D-Var, are relatively seldom used within climate analysis frameworks, partly because of the enormous numerical costs. In order to face this issue ESA's high performance computing Grid on-Demand (G-POD) is used. We assimilate Global Navigation Satellite System (GNSS) based radio occultations (RO). RO data in general exhibit some favorable properties, like global coverage, all-weather capability expected long-term stability and accuracy. These properties and the continuity of data offered by the Meteorological Operational Satellite (MetOp) program and other RO missions are an ideal opportunity to study the long term atmospheric and climate variability.This paper investigates the assimilation of RO refractivity profiles into first guess fields derived from 21 years of ECMWF's ERA40 dataset on a monthly mean basis divided into four synoptic time layers in order to take the diurnal cycle into account. In contrast to NWP systems, the assimilation procedure is applied without cycling, thus enabling us to run our 3D-Var implementation within G-POD parallel for different time layers. Results indicate a significant analysis increment which is partly systematic, emphasizing the ability of RO data to add independent information to ECMWF analysis fields, with a potential to correct biases. This work lays the ground for further studies using data from existing instruments within a framework based on a mature methodology.     

Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation

In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature, H, and latent heat flux (LE) collected at the Yucheng (longitude: 116° 36′ E; latitude: 36° 57′ N) and Arou (longitude: 100° 27′ E; latitude: 38° 02′ N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST and H data assimilation schemes could improve the estimation of ground temperature and H. The root mean square error (RMSE) compared between the predictions and in situ measurements decreased more significantly with the assimilation of values of H measured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions of H and ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view.     

Sensitivity analysis of high-resolution Oceansat-2 scatterometer winds on Thane cyclone simulation

Ocean surface wind vectors retrieved from the Oceansat-2 scatterometer (OSCAT) are used in this study to evaluate their impact on Thane cyclone simulation. The Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-Var) data assimilation system are adapted to evaluate the sensitivity of OSCAT observations. Simulated track error and landfall forecast are considered as standard measurements to assess the impact of 50 km and ~15 km spacing grid OSCAT winds along and across the swath. Significant improvement is obtained in track forecasting, when high-resolution vector winds (HVW; composite slice-level winds, ~15 km) are used for assimilation rather than coarser-resolution (50 km) operational OSCAT winds. Forecasting sensitivity to observations (OSCAT winds) using WRF tangent linear and adjoint modelling is used to quantify the impact of two different resolutions of OSCAT winds. WRF adjoint modelling is used here as a diagnostic tool, which indicates that high-resolution OSCAT winds have a more positive impact on the track prediction of Thane tropical cyclone.     

Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study

Predicted latent and sensible heat fluxes from Land Surface Models (LSMs) are important lower boundary conditions for numerical weather prediction. While assimilation of remotely sensed surface soil moisture is a proven approach for improving root zone soil moisture, and presumably latent (LE) and sensible (H) heat flux predictions from LSMs, limitations in model physics and over-parameterisation mean that physically realistic soil moisture in LSMs will not necessarily achieve optimal heat flux predictions. Moreover, the potential for improved LE and H predictions from the assimilation of LE and H observations has received little attention by the scientific community, and is tested here with synthetic twin experiments. A one-dimensional single column LSM was used in 3-month long experiments, with observations of LE, H, surface soil moisture and skin temperature (from which LE and H are typically derived) sampled from truth model run outputs generated with realistic data inputs. Typical measurement errors were prescribed and observation data sets separately assimilated into a degraded model run using an Ensemble Kalman Filter (EnKF) algorithm, over temporal scales representative of available remotely sensed data. Root Mean Squared Error (RMSE) between assimilation and truth model outputs across the experiment period were examined to evaluate LE, H, and root zone soil moisture and temperature retrieval. Compared to surface soil moisture assimilation as will be available from SMOS (every 3 days), assimilation of LE and/or H using a best case MODIS scenario (twice daily) achieved overall better predictions for LE and comparable H predictions, while achieving poorer soil moisture predictions. Twice daily skin temperature assimilation achieved comparable heat flux predictions to LE and/or H assimilation. Fortnightly (Landsat) assimilations of LE, H and skin temperature performed worse than 3-day moisture assimilation. While the different spatial resolutions of these remote sensing data have been ignored, the potential for LE and H assimilation to improve model predicted LE and H is clearly demonstrated.     

Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

Advanced information on crop yield is important for crop management and food policy making. A data assimilation approach was developed to integrate remotely sensed data with a crop growth model for crop yield estimation. The objective was to model the crop yield when the input data for the crop growth model are inadequate, and to make the yield forecast in the middle of the growing season. The Cropping System Model (CSM)–Crop Environment Resource Synthesis (CERES)–Maize and the Markov Chain canopy Reflectance Model (MCRM) were coupled in the data assimilation process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and vegetation index products were assimilated into the coupled model to estimate corn yield in Indiana, USA. Five different assimilation schemes were tested to study the effect of using different control variables: independent usage of LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and synergic usage of LAI and EVI or NDVI. Parameters of the CSM–CERES–Maize model were initiated with the remotely sensed data to estimate corn yield for each county of Indiana. Our results showed that the estimated corn yield agreed very well with the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data. Among different scenarios, the best results were obtained when both MODIS vegetation index and LAI products were assimilated and the relative deviations from the NASS data were less than 3.5%. Including only LAI in the model performed moderately well with a relative difference of 8.6%. The results from using only EVI or NDVI were unacceptable, as the deviations were as high as 21% and ?13% for the EVI and NDVI schemes, respectively. Our study showed that corn yield at harvest could be successfully predicted using only a partial year of remotely sensed data.     

Impact of Megha-Tropiques SAPHIR radiances in T574L64 global data assimilation and forecasting system at NCMRWF

The Sounder for Probing Vertical Profiles of Humidity (SAPHIR) is a sounding instrument of Megha-Tropiques (Indo-French joint satellite); launched by the Indian Space Research Organization on 12 October 2011 with six channels near the absorption band of water vapour at 183 GHz. In the framework of this work, the assimilation scheme has been first modified to enable the SAPHIR radiance observations being used as additional observation type, and second, a methodology has been prepared to remove the radiance pixels significantly affected by clouds. The impact of SAPHIR radiances on analysis as well as forecasts of the National Centre for Medium Range Weather Forecasting-Global Forecast System (NGFS) at T574L64 resolution has been investigated through data assimilation. Measurements from SAPHIR are incorporated into the Gridpoint Statistical Interpolation three-dimensional variational assimilation system to provide the improved initial conditions. To find out the impact, analysis/forecast cycling experiments with and without SAPHIR radiances are performed during the period 10–29 November 2013. The impact of the improvement in term of root mean square error has been clearly evaluated for five parameters, namely, relative humidity, temperature, wind, geopotential height, and specific humidity. It is demonstrated that the assimilation of SAPHIR observations results in a considerable improvement for the five parameters over the global region. During the study period, two tropical cyclones (HELEN, 19–22 November and LEHAR, 23–28 November) were formed over the North Indian Ocean. Impact on specific humidity and track forecast errors of tropical cyclone are also examined. Overall, initial results show the usefulness of SAPHIR radiances in the NGFS.     

On some aspects of precipitation over the tropical Indian Ocean using satellite data

The annual and inter‐annual variability of precipitation over the tropical Indian Ocean is studied for the period 1979–1997, using satellite data from a variety of sensors. The Climate Prediction Center Merged Analysis Precipitation (CMAP), Microwave Sounding Unit (MSU) estimates of rainfall had better correlation with the island rainfall data than the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NRA) estimates. A comparison of the mean annual rainfall by different estimates (CMAP, MSU, NRA and GPCP (Global Precipitation Climatology Programme)) showed significant differences with the CMAP, GPCP and MSU estimates depicting maximum off the Indonesian Islands whilst the NRA exhibited maximum in the southern part of the Bay of Bengal and equatorial Indian Ocean. A study of the inter‐annual variability of the monsoon rainfall using the monthly CMAP data over the tropical Indian Ocean for different study areas, namely, Arabian Sea (AS), Bay of Bengal (BB), south Indian Ocean (SIO) and Indian Ocean (IO) showed significant differences during deficit years (1979, 1982, 1986 and 1987), excess monsoon years (1983 and 1988) and also during El Nino Southern Oscillation (ENSO) years (1982, 1987, 1992 and 1997). An analysis of the rainfall anomalies showed positive and negative anomalies in the north‐eastern Bay of Bengal during the summer season of deficit (1986) and excess (1988) monsoon years respectively, whilst the eastern equatorial Indian Ocean showed large positive and negative rainfall anomalies during the autumn season of El Niño years, 1987 (deficit monsoon) and 1997 (normal monsoon) respectively.