Combining vLAPS and Nudging Data Assimilation

Abstract

The combination of techniques that incorporate observational data may improve numerical weather prediction forecasts; thus, in this study, the methodology and potential value of one such combination were investigated. A series of experiments on a single case day was used to explore a 3DVAR-based technique (the variational version of the Local Analysis and Prediction System; vLAPS) in combination with Newtonian relaxation (observation and analysis nudging) for simulating moist convection in the Advanced Research version of the Weather Research and Forecasting model. Experiments were carried out with various combinations of vLAPS and nudging for a series of forecast start times. A limited subjective analysis of reflectivity suggested all experiments generally performed similarly in reproducing the overall convective structures. Objective verification indicated that applying vLAPS analyses without nudging performs best during the 0–2 h forecast in terms of placement of moist convection but worst in the 3–5 h forecast and quickly develops the most substantial overforecast bias. The analyses used for analysis nudging were at much finer temporal and spatial scales than usually used in pre-forecast analysis nudging, and the results suggest that further research is needed on how to best apply analysis nudging of analyses at these scales.

A local observation data assimilation in mesoscale numerical weather prediction models

“…Here G q is the coefficient of influence (nudging strength, = 6*10 -4 1/ s), N is the number of assimilated observations, W q is a weight function representing the temporal and spatial relationship between the grid nodes and observations. Figure 4 shows the numerical prediction results obtained using the WRF model without additional observation data assimilation from the Atmosphere JUC and with assimilation using the "observation nudging" technology [16]. Also, this figure presents (for comparison) the observation results of near-ground values of air temperature, wind speed and direction.…”

Applying pySTEPS optical flow algorithms to improve convection nowcasting over the Maritime Continent

“…Weather radar Australia (Seed, 2003) Global Synthetic Weather Radar (GSWR) Satellite, lightning, NWP US (Reen et al, 2020) Table 1. Information on some of the state-of-the-art nowcasting systems that are currently in operational use around the world.…”