Impact of data assimilation on high‐resolution rainfall forecasts: A spatial, seasonal, and category analysis

Abstract

In a limited area model (LAM), the impact of data assimilation is likely to depend on the background state through lateral boundary forcing; this may introduce certain seasonality in the impact of data assimilation on rainfall forecasting. It is also likely that the impact of data assimilation on forecasts will have certain spatial variability. Finally, owing to the convective nature of rainfall and the roles of parameterization scheme, the impact of data assimilation may depend on the category (intensity) of rainfall. Here these aspects for rainfall forecasts at high resolution were examined. Using a LAM (An advanced version of Weather Research and Forecasting Model), we have carried out twin simulations with and without data assimilation; the simulations without data assimilation are used as the benchmark for assessing the impact of data assimilation. Analysis of simulations for 40 sample days distributed over the years 2012-2014 over Karnataka (southern state in India) is carried out to estimate impact of data assimilation. Various statistical measures show that data assimilation improved the rainfall prediction in most cases; however, there is also strong seasonality and location dependence in impact of data assimilation. Our results also show that improvement due to data assimilation is higher/lower for lower/higher rainfall categories. Analysis shows that the cases where the initial states with data assimilation depart strongly from the first guess generally result in less or even negative impact. It is pointed out that the results have important implications in design of observation system and assessment of impact of forecasts.

Observed and model‐simulated thermodynamic processes associated with urban heavy rainfall events over Bangalore, India

“…The high-resolution rainfall forecasts at hobli (a cluster of villages each having an area of about 10 km 2 ) level were verified against the observations at comparable resolution from the telemetric rain-gauge (TRG) network installed and maintained by the KSNDMC; the study area of Bangalore consists of 34 hobli divisions. There were 52 rain gauges over Bangalore, and since they were not uniformly distributed, a Thiessen polygon method was used to average the data to obtain the rainfall for each hobli at a horizontal resolution of the order of 10 km 2 (Rakesh and Goswami, 2015). In the present study, the 24 hraccumulated rainfall forecasts over the 34 hobli divisions in Bangalore extracted from domain 4 were verified against the corresponding rain-gauge observations.…”

“…From 36 hr forecasts, the initial 0300 hours were not considered due to model spin-up and the next 24 hr (valid for 0300 to 0300 UTC of the next day)-accumulated rainfall was used for validations. Interpolated (bilinear interpolation) model values at the TRG locations were used to obtain average (Thiessen polygon method) rainfall over hobli divisions from the model forecasts (Rakesh and Goswami, 2015). Thermodynamic stability indices calculated from the model were validated with a moderateresolution imaging spectroradiometer (MODIS).…”

“…The simulations were carried out for 32 rainfall events over Bangalore where the model was initiated at 0000 UTC for each case with a 36 hr period of integration. The three hourly forecast data, archived in near real-time from the NCEP Global Forecast System (GFS) (0.5 × 0.5 ) (NCEP/NCAR), enhanced by the assimilation of local observations such as radiosondes, telemetric weather station (TWS) installed by the Karnataka State Natural Disaster Monitoring Centre (KSNDMC), automatic weather stations (AWS) observations from the India Meteorological Department (IMD), and CSIR Climate Observation and Modelling Network (COMoN) using the WRF threedimensional variational (3D-Var) assimilation scheme, was used to generate the model's initial conditions (Rakesh and Goswami, 2015). The basic model parameters such as temperature, wind vector, pressure and humidity from the TWS observations were assimilated in the model outer domain (domain 1).…”

“…The profiler data from upper air soundings (about 70 locations) over South Asia from the University of Wyoming (http://weather.uwyo.edu) were also assimilated in domain 1. The model background and observation errors covariances used in the present study were similar to those used by Rakesh and Goswami (2015). The model landsurface parameters such as terrain height, land-use (USGS-25 Category) and vegetation fraction were extracted from the US Geological Survey data sets.…”

“…Higher resolution models also help to make use of highresolution input data (Miao et al, 2009). Rakesh and Goswami (2015) examined the impact of assimilation of in-situ data on the simulation of heavy rainfall events over the Indian region and showed that there are strong seasonality and location dependence in the impact of data assimilation. Mohapatra et al (2017) examined the skill of a high-resolution configuration of the WRF model when simulating localized and non-localized heavy rainfall events over Bangalore and found that model skill was relatively poor in the case of localized rainfall events.…”