Examination of a Real-Time 3DVAR Analysis System in the Hazardous Weather Testbed

Ren

et al. 2016

Preprint

Abstract. A survey was performed to collect 92 seasonal snow samples across northern China in January 2014. The results show that mixing ratios of black carbon (BC), organic carbon (OC), and anthropogenic dust (AD) range from 60 to 4200&#8201;ng&#8201;g&#8722;1, 280 to 32&#8201;000&#8201;ng&#8201;g&#8722;1, and 1300 to 5800&#8201;ng&#8201;g&#8722;1 in surface snow, respectively. A relatively high correlation (R2&#8201;=&#8201;0.87, n&#8201;=&#8201;13) between OC and BC in surface snow is observed, indicating similar emission sources, and high OC/BC ratios suggest that biomass burning may be a major contributor at most snow sampling sites. In addition to the BC and AD parameters in the Snow, Ice, and Aerosol Radiation (SNICAR) model, the OC content in snow is considered an initial parameter for calculating snow albedo through a new radiative transfer model (Spectral Albedo Model for Dirty Snow, or SAMDS). The spectral albedo of snow reduction caused by OC (20&#8201;&#956;g&#8201;g&#8722;1) is up to a factor of 3 for a snow grain size of 800&#8201;&#956;m compared to 100&#8201;&#956;m. We find a larger difference in snow albedo levels between the model simulations and surface measurements for higher insoluble light-absorbing impurities (ILAPs) using the measured snow grain radii. However, using the optical effective radii (Reff), snow albedos modeled through both the SNICAR and SAMDS models are consistent with surface measurements, especially in the case of near-infrared wavelengths.

Section: The Spatial Distribution Of Aod 10mentioning

confidence: 99%

Snow albedo reduction in seasonal snow due to anthropogenic dust and carbonaceous aerosols across northern China

Ren

et al. 2016

Preprint

“…Considering the lower computational cost of a single deterministic run, cloud‐resolving horizontal grid spacing (1–1.5 km) was chosen for the deterministic system. The 3DVAR analysis system has been tested during the HWT Experimental Warning Program (EWP) from 2011 to 2013, and evaluations demonstrated an overall improved skill in analyzing storm‐scale structures along with the timing and location of the most intense storm‐scale objects in the forecast domain (Gao et al ., 2013; Calhoun et al ., 2014; Smith et al ., 2014). These developments and associated improvements, however, were mainly achieved by the systematic assimilation of radar radial velocity and reflectivity data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an experimental Warn‐on‐Forecast 3DVAR analysis and forecast system on quasi‐real‐time short‐term forecasts of high‐impact weather events

Gao

Quart J Royal Meteoro Soc

et al. 2021

Self Cite

The purpose of this study is to demonstrate the capability of an experimental, weather-adaptive, high-resolution, deterministic Warn-on-Forecast (WoF) analysis and forecast system (WoF3DVAR-AFS) for predicting high-impact severe weather events that occurred during the Hazardous Weather Testbed 2019 Spring Forecast Experiments. WoF3DVAR-AFS uses a three-dimensional variational (3DVAR) method as its core data assimilation system and the Advanced Research Version of the Weather Research and Forecasting (WRF-ARW) model as its forward model. Surface measurements provided in meteorological aviation reports and the Oklahoma Mesonet, Doppler radar data, and spaceborne total lightning observations provided by the Geostationary Lightning Mapper are assimilated at 15-min frequency over a target domain determined by the "Day 1" Convective Outlook product from the Storm Prediction Center. The chief goal of this system is to complement probabilistic forecasts generated by ensemble analysis and forecast systems, such as the experimental Warn-on-Forecast System (WoFS) with a higher-resolution deterministic member to aid forecasters' decision-making. We performed both qualitative and quantitative evaluations on 0-6 hr forecasts launched hourly from 1900 to 0300 UTC the next day for each of the 12 cases. Aggregated subjective forecast evaluation metrics from each individual case, as well as detailed comparison against available verification datasets, suggest that the forecasts are generally skillful in terms of composite reflectivity fields, quantitative precipitation forecasts, and the strength and location of rotation tracks and damaging winds. This study presents initial efforts to assess the performance of WoF3DVAR-AFS and provides possible directions for further improvements, including the development of a weather-adaptive, dual-resolution analysis and forecast system hybrid with an ensemble system, such as the experimental Warn-on-Forecast system.

“…To assess the potential value of the weather-adaptive, real-time analysis system to warning operations, it was formally tested and evaluated by forecasters who participated in the NOAA Hazardous Weather Testbed (HWT) spring experiments from 2011 to 2013. During this period, many severe weather events were successfully detected and analyzed automatically by this 3DVAR system (Gao et al 2013;Smith et al 2014;Calhoun et al 2014). The analyzed storm structures not only matched quite well with synthesized reflectivity fields from multiple radars, but the most intense storms corresponded with the location and time of the National Weather Service (NWS) local storm reports.…”

Section: Introductionmentioning

confidence: 78%

“…Forecast evaluation is conducted with quantitative metrics and subjective evaluation. The quantitative metrics include the neighborhood equitable threat score (nETS; Gilbert 1884; Clark et al 2010) and frequency bias for the composite reflectivity that is verified against observations obtained from NSSL Multi-Radar Multi-Sensor System (MRMS; Smith et al 2016). The subjective comparisons are performed with the NWS local storm reports for the tracks of the updraft helicity (UH) between 2 and 5 km above ground level (Kain et al 2008(Kain et al , 2010 and the MRMS observations for reflectivity, respectively.…”

Section: Introductionmentioning

confidence: 99%

Test of a Weather-Adaptive Dual-Resolution Hybrid Warn-on-Forecast Analysis and Forecast System for Several Severe Weather Events

Weather and Forecasting

Gao

Skinner

et al. 2019

Self Cite

A real-time, weather adaptive, dual-resolution, hybrid Warn-on-Forecast (WoF) analysis and forecast system using the WRF-ARW forecast model has been developed and implemented. The system includes two components, an ensemble analysis and forecast component, and a deterministic hybrid three-dimensional ensemble–variational (3DEnVAR) analysis and forecast component. The goal of the system is to provide on-demand, ensemble-based, and physically consistent gridded analysis and forecast products to forecasters for making warning decisions. Both components, the WRF-DART system with 36 ensemble members and the hybrid 3DEnVAR system, assimilate radar data, satellite-retrieved cloud water path, and surface observations at 15-min intervals with dual-resolution capability. In the current hybrid configuration, one-way coupling of the two analysis systems is performed: ensemble covariances derived from the WRF-DART system are incorporated into the hybrid 3DEnVAR system with each data assimilation (DA) cycle. This study examines deterministic, 3-h forecasts launched from the hybrid 3DEnVAR analyses every 30 min for three severe weather events in 2017. The performance of the deterministic component is evaluated for four configurations: dual-resolution coupling, single-resolution coupling, forecasts initialized using a cloud analysis for reflectivity assimilation, and forecasts initialized from the WRF-DART ensemble mean. Quantitative and subjective evaluation of composite reflectivity and updraft helicity (UH) swath forecasts for the three events indicate that the dual-resolution strategy without the cloud analysis performs best among the four configurations and provides the most realistic prediction of reflectivity patterns and UH tracks.