Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System

Abstract

Current methods of assimilation of precipitation into numerical weather prediction models are able to make the model precipitation become similar to the observed precipitation during the assimilation, but the model forecasts tend to return to their original solution after a few hours. To facilitate the precipitation assimilation, a logarithm transformation has been used in several past studies. Lien et al. proposed instead to assimilate precipitation using the local ensemble transform Kalman filter (LETKF) with a Gaussian transformation technique and succeeded in improving the model forecasts in perfect-model observing system simulation experiments (OSSEs). In this study, the method of Lien et al. is tested within a more realistic configuration: the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecast System (GFS). With guidance from a statistical study comparing the GFS model background precipitation and the TMPA data, some modifications of the assimilation methods proposed in Lien et al. are made, including 1) applying separate Gaussian transformations to model and to observational precipitation based on their own cumulative distribution functions; 2) adopting a quality control criterion based on the correlation between the long-term model and observed precipitation data at the observation location; and 3) proposing a new method to define the transformation of zero precipitation that takes into account the zero precipitation probability in the background ensemble rather than the climatology. With these modifications, the assimilation of the TMPA precipitation data improves both the analysis and 5-day model forecasts when compared with a control experiment assimilating only rawinsonde data.

Accelerating assimilation development for new observing systems using EFSO

“…It is conducted for 13 months from 00:00 UTC 1 December 2007 to 00:00 UTC 1 January 2009. It is identical to the "RAOBS" experiment in Lien et al (2016b), where more details can be found. This CON-TROL serves as the basis of the following non-cycled EFSO calculation (Step 1), and as the reference of the cycled OSEs (Step 3).…”

“…Assimilation of all precipitation data without special treatments usually leads to no impacts or negative impacts. In spite of these issues, Lien et al (2013Lien et al ( , 2016b and Kotsuki et al (2017) conducted a series of experiments, from an idealized configuration to realistic systems, to show that it is possible to improve the medium-range forecasts in a global model by the assimilation of global precipitation data. The keys in their experiments are to use 1. an LETKF to exploit the flow-dependent background error correlation between prognostic variables and diagnosed precipitation;…”

“…We use the same system as in Lien et al (2016b), assimilating the global TMPA data into a low-resolution version of the NCEP Global Forecast System (GFS) model with the LETKF. The model resolution is spectral T62 with 64 vertical levels.…”

“…Namely, the TMPA data are upscaled to the low-resolution T62 GFS grids and temporally integrated into 6 h accumulation amounts. The Gaussian transformations for the model/observation precipitation variables (i.e., the "GTbz" method in Lien et al, 2016b) are applied. Other details of the experimental configuration and the Gaussian transformation method are discussed in Lien et al (2016b).…”

“…The Gaussian transformations for the model/observation precipitation variables (i.e., the "GTbz" method in Lien et al, 2016b) are applied. Other details of the experimental configuration and the Gaussian transformation method are discussed in Lien et al (2016b). These techniques are essential to achieve basic satisfactory performance of the TMPA assimilation; however, even with them, assimilating all available data does not lead to an optimal result.…”