Data Assimilation & Prediction

(COMPLETED) Evaluation of the Newly Developed Observation Operators for Assimilating Satellite Cloud and Precipitation Observations in GSI within the HWRF system

Project Period: May 1, 2017 to April 30, 2018
Principal Investigator(s): Ting-Chi Wu
Co-Principal Investigator(s): Milija Zupanski
Sponsor(s): Developmental Testbed Center (DTC)


Data assimilation has contributed greatly to the improvement of numerical weather prediction (NWP) by optimally combining information from a forecast model with available observations. Among the available observations from various platforms that are assimilated in operational NWP centers, satellite measurements of cloud and precipitation information are currently not being fully exploited to their maximum capacity. Since the high-impact weather events such as hurricanes are often associated with clouds and precipitation, assimilating the satellite measured cloud and precipitation information may further improve the hurricane forecasting.


Proposed Work: Although the HWRF/GSI capabilities to assimilate satellite retrieved water contents have already been developed, the assimilation impact has only been preliminarily tested (e.g., Wu et al. 2016). In this project, however, we plan to perform a detailed evaluation of the impact of the new observation operators by employing an extensive set of selected cases (i.e., larger sample size) and assessing their value in a systematic manner before they are ready for general/public use. The very first step is to merge these capabilities into the GSI and HWRF trunk codes, where all the new developments, code updates and maintenances are retained. We will then conduct a set of tests that include experiments with and without the activation of the newly added capabilities to confirm the stability of the system with the added features. To offer a robust performance statistics, we will apply the added capabilities to the pre-selected set of cases to evaluate their effectiveness. Once the proposed testing and evaluation are completed, these new capabilities based our recent research efforts (Wu et al. 2016, Wu and Zupanski, 2017) will be available for operational considerations. With that, we anticipate a rather smooth research to operation (R2O) transition.


Final Report