by Ajay Bankar, V. Rakesh & Smrati Purwar
This study explores the impact of assimilating diverse observational data on forecasting extreme rainfall events (EREs) using a three dimensional variational (3D-Var) assimilation approach. It focuses on 38 EREs across three meteorological divisions in Karnataka, India, using a high-resolution (03-km) Weather Research and Forecasting (WRF) model with three nested domains. Five distinct experiments were conducted, including a Control experiment without assimilation, and subsequent experiments integrating observations from various sources like atmospheric profiles from Atmospheric InfraRed Sounder (AIRS) and Moderate resolution Imaging Spectroradiometer (MODIS) satellites and radiosondes, ocean surface wind observations from Advanced Scatterometer (ASCAT), Special Sensor Microwave Imager (SSMI), and WindSAT satellites and buoys, ground observations from Karnataka State Natural Disaster Monitoring Centre (KSNDMC), as well as a combined assimilation experiment with all available observations. The accuracy of rainfall forecasts is evaluated by comparing model outputs with high-resolution telemetric rain-gauge (TRG; 6480 stations) data and other meteorological parameters against telemetric weather station (TWS; 860 stations) data from KSNDMC. Assimilation experiments show positive improvements over control experiment in predicting rainfall. Results consistently indicate underprediction of rainfall in the intricate topographical region of the Western Ghats (WG) across all experiments, contrasting with overprediction along the coastal areas of Karnataka. The experiment involving Ocean Winds showcased a substantial 40 % reduction in rainfall overprediction (above 2 mm threshold). Both Ocean Winds and Station Data assimilation notably enhanced rainfall prediction accuracy over most of the regions in Karnataka, with Ocean Winds exhibiting the highest improvement (53 %), closely followed by Station Data (50 %). Importantly, assimilating Ocean Winds and Station Data aided in reducing overprediction, while assimilating Satellite Profiles reduced underprediction in the interior part of Karnataka but increased overprediction over the coastal region compared to the control experiment. Frequency of occurrence of rainfall is considerably enhanced along the coastline in all 3D-Var experiments. Bias score indicates maximum improvement in assimilation using Ocean Winds and Station Data. Simulation of basic meteorological parameters also improved with assimilation particularly during the day hours. The results underscore the crucial role of assimilation of satellite and in-situ observations in improving forecast accuracy of EREs during the monsoon season.
Source: https://doi.org/10.1016/j.atmosres.2024.107777
by Priyanshi Singhai, Arindam Chakraborty, Kaushik Jana, Kavirajan Rajendran, Sajani Surendran and Kathy Pegion
An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. Here, we highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version 2 (CFSv2). While all ensemble members often agree on the sign of predicted El Niño Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in some of the individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO signals significantly influences moisture convergence and Indian summer monsoon rainfall (ISMR). This stronger influence of ENSO on seasonal predictions increases ENSO–ISMR correlation in ensemble mean seasonal hindcasts. Thus, this discrepancy in the ENSO–ISMR relationship is not present in the individual ensemble members, considered individually or together (without averaging) as independent realizations. Therefore, adequate care should be taken while evaluating physical mechanisms of teleconnection in ensemble mean predictions that can often be skewed due to constructive or destructive superposition of different impacts.
Source: https://doi.org/10.1016/j.dynatmoce.2024.101504
