8A.1 Linking Weather Research and Operations to Field Forecasting Needs through Operational Requirements: An Example with Mesoscale Analysis

Wednesday, 15 January 2020: 8:30 AM
252A (Boston Convention and Exhibition Center)
Young-Joon Kim, NWS, Silver Spring, MD; and M. A. Tew

The NWS’ mesoscale analysis system, Real-Time Mesoscale Analysis/UnRestricted Mesoscale Analysis (RTMA/URMA), is a key tool that provides Analysis of Record for forecasters. However, as testified by field leaders and forecasters, RTMA/URMA suffers from degraded quality over some areas such as complex terrain where terrain variation is large. The Analysis and Nowcast Branch of NWS performed a gap analysis of this quality degradation by investigating various field reports and testimonies as well as survey results.

The quality of mesoscale analysis directly affects downstream systems such as the National Blend of Models (NBM) that rely heavily on URMA as its calibration and validation data source. In fact, the quality of NBM depends critically on what observation/analysis dataset is used as verification, which makes NBM evaluation challenging and ad hoc without a reliable reference.

The degraded quality of URMA can originate from biases in two kinds of upstream systems – forecast model and observation data quality control / bias correction system. In order to systematically alleviate the biases of URMA over complex terrain, the systematic biases present in these upstream systems (e.g., High-Resolution Rapid Refresh model or HRRR) must be systematically reduced. In addition, observations from such networks as Mesonet have serious quality issues – while data are abundant – at some stations due to its ad hoc methods for quality control such as flagging of bad station data based on episodic reports over complex terrain. For systematically reducing these biases, these algorithms need to be science-based by taking into account highly-varying physical characteristics of flow over complex terrain.

In this work, we present our rationale behind and efforts on how to scientifically develop modeling requirements based on our gap analysis of the field’s needs. This talk will also serve as an introduction to the Session “Improving R2O & O2R in the 0–18 Hour Forecast Range Linking Research and Operations to Forecasters’ Needs” of the Tenth Conference on the Transition of Research to Operations.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner