Anoop Mishra

Use of APHRODITE Rain Gauge-Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method

AbstractA multimodel superensemble developed by the Florida State University combines multiple model forecasts based on their past performance (training phase) to make a consensus forecast. Because observed precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the Asian monsoon region (0°–50°N, 60°–150°E) and results of four coupled climate models were used. TRMM 3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic superensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast was excluded from...

Rainfall Estimation from Combined Observations Using KALPANA-IR and TRMM- Precipitation Radar Measurements over Indian Region

In the present study an attempt has been made to improve the rainfall estimation technique developed recently by Mishra et al. (2009a, 2009b) based on KALPANA and Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) data over the Indian land and oceanic region. The algorithm for rainfall estimation was basically based on synergistically analyzing the thermal infra-red radiances from Kalpana/INSAT data along with the high resolution, horizontal and vertical rainfall estimates from PR. Presently the augmentation is based on the data base of precipitable water and relative humidity from National Centre for Environmental Prediction-Global forecast System (NCEP-GFS) data as a background field to correct for the biases in earlier algorithm. The algorithm is tested for many case studies of monsoon rainfall over India and adjoining oceanic regions. The rainfall from the present scheme is compared with the standard TRMM-3B42 rain product. The validation with the Automatic Weather Station (AWS) rain gauge and the Global Precipitation and Climatology Project (GPCP) version 2 rain products shows that the present scheme is able to retrieve the rainfall with a very good accuracy. These studies are aimed at the rainfall retrievals in near future from both INSAT-3D and Megha-Tropiques, IR and MW imagers respectively.

Evaluation of Precipitation Features in High-Frequency SSM/I Measurements Over Indian Land and Oceanic Regions

Evaluation of the Special Sensor Microwave Imager (SSM/I) precipitation features is presented from various passes over Indian oceanic and land regions under the framework of radiative transfer simulations for both emission and scattering atmospheres. There is considerable uncertainty in the interpretation of the SSM/I high-frequency scattering features for development of rainfall algorithms. Specifically large areas of very low emissivity regimes showing false rain signatures due to the presence of low brightness temperatures (TBs) that are often present in the vicinity of colder sea surface areas in the 85-GHz TB from SSM/I. In this connection, the Polarization Corrected Temperature (PCT) defined by Spencer using the 85-GHz channels, vertical (V) and horizontal (H), has been studied to delineate these surface effects. These false scattering signatures, once corrected using PCT as a suitable parameter, significantly improve the quality of the SSM/I-derived precipitation areas. These results are also confirmed with the cloud optical depth data from the Moderate Resolution Imaging Spectroradiometer sensor onboard the Aqua satellite and the standard merged rain product (geostationary infrared and Tropical Rainfall Measuring Mission (TRMM) microwave data) 3B42 from TRMM. This letter is aimed for selecting PCT as one of the suitable predictor variables for the development of operational rainfall retrieval algorithm for the Indo-French Megha-Tropiques satellites microwave radiometer at high frequencies.

Study of Rainfall from TRMM Microwave Imager Observation over India

This paper presents a technique to estimate precipitation over Indian land (6–36°N, 65–99°E) at spatial grid using tropical rainfall measuring mission (TRMM) microwave imager (TMI) observations. It adopts the methodology recently developed by Mishra (2012) to monitor the rainfall over the land portion. Regional scattering index (SI) developed for Indian region and polarization corrected temperature (PCT) have been utilized in this study. These proxy rain variables (i.e., PCT and SI) are matched with rainfall from precipitation radar (PR) to relate rain rate with PCT, SI, and their combination. Retrieval techniques have been developed using nonlinear relationship between rain and proxy variables. The results have been compared with the observations (independent of training data set) from PR. Results have also been validated with the observations from automatic weather station (AWS) rain gauges. It is observed from the validation results that nonlinear algorithm using single variable SI underestimates the low rainfall rates (below 20 mm/h) but overestimates the high rain rates (above 20 mm/h). On the other hand, algorithm using PCT overestimates the high rain rates (above 25 mm/h). Validation results with rain gauges show a CC of 0.68 and RMSE of 4.76 mm when both SI and PCT are used.

Comparison of TRMM TMI and PR Version 5 and 6 Precipitation Data Products Under Cyclonic Weather Conditions

In 2004, the Tropical Rainfall Measuring Mission (TRMM) Science Project released a newer version of precipitation products, i.e., version 6 (V6), from its various instruments. The V6 data products are expected to be more accurate than the previous version 5 (V5). In this letter, we have attempted to analyze V5 and V6 products from two primary sensors on the TRMM, namely, the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR), to unravel the quality of the V6 products vis-a-vis that of the V5 products. It is found that there are significant changes in the TMI-derived precipitation values, but the TMI brightness temperature (Tb) has not undergone any change from V5 to V6. Thus, the retrieval algorithm must have undergone some changes from V5 to V6. While the total number of TMI raining pixels is nearly unchanged, V5 moderate rain events (1 - 5 mm ldr h-1) are more often classified as low rain events (< 1 mm ldr h-1) in V6. Thus, TMI-based precipitation shows larger bias, particularly at low to moderate rain rates than that in V5. This results in a depression in the average Tb at around 2 mm ldr h-1 in the Tb-rain-rate relationship with V6 measurements, which is implausible. This dip in average Tb value further complicates the Tb-rain-rate relationship by showing a continuous rise in Tb with rain rate, which is again implausible. On the other hand, PR-based rain rates have not undergone much change from V5 to V6. The relationship between Tb from TMI and rain rate from PR also does not show any anomalous behavior.

Retrieval of EVI from Oceansat 2 Data and Comparison with MODIS Derived EVI

The vegetation index is derived using many remote sensing sensors. Vegetation Index is extensively used and remote sensing has become the primary data source. Number of vegetation indices (VIs) have been developed during the past decades in order to assess the state of vegetation qualitatively and quantitatively. Analysis of vegetation indices has been carried out by many investigators scaling from regional level to global level using the remote sensing data of varying spatial, temporal and radiometric resolutions. There are as many as 14 VIs in use. Globally operational algorithms for generation of NDVI have utilized digital counts, at sensor radiances, ‘normalized’ reflectance (top of the atmosphere), and more recently, partially atmospheric corrected (ozone absorption and molecular scattering) reflectance. Presently NDVI and EVI are standard MODIS data products which are widely used by the scientific community for environmental studies. The OCM sensor in Oceansat 2 is designed for ocean colour studies. The OCM sensor has been used for studying ocean phytoplankton, suspended sediments and aerosol optical depth by many investigators. In addition to its capability of studying the ocean surface, OCM sensor has also the potential to study the land surface features. In a past EVI has been retrieved using OCM sensor of Oceansat 1. However, there is slight change in the band width of Oceansat 2—OCM sensor compared with OCM of Oceansat 1 sensor. In the present paper an attempt has been made to derive EVI using Oceansat 2 OCM sensor and the results have been compared with MODIS data. The enhanced vegetation index (EVI) is calculated using the reflectance values obtained after removing molecular scattering and ozone absorption component from the total radiance detected by the sensor. The band-2, Band-3, band-6 and band-8 corresponding to Blue, Red and Infrared part of the visible spectrum have been used to determine EVI. The result shows that Oceansat 2 derived EVI and MODIS derived EVI are well correlated.