Operational Precipitation Forecast Over China Using the Weather Research and Forecasting (WRF) Model at a Gray-Zone Resolution: Impact of Convection Parameterization

2021 ◽  
Author(s):  
XU ZHANG ◽  
YUHUA YANG ◽  
BAODE CHEN ◽  
WEI HUANG
Keyword(s):  
Case Studies ◽  
Southern China ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Operational System ◽  
Quantitative Precipitation Forecast ◽  
Geographical Regions ◽  
Convection Parameterization

AbstractThe quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

Neighborhood-Based Verification of Precipitation Forecasts from Convection-Allowing NCAR WRF Model Simulations and the Operational NAM

2010 ◽  
Vol 25 (5) ◽  
pp. 1495-1509 ◽  
Author(s):  
Adam J. Clark ◽  
William A. Gallus ◽  
Morris L. Weisman
Keyword(s):  
Spatial Scale ◽  
Large Scale ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Central United States ◽  
Before And After ◽  
Grid Points

Abstract Since 2003 the National Center for Atmospheric Research (NCAR) has been running various experimental convection-allowing configurations of the Weather Research and Forecasting Model (WRF) for domains covering a large portion of the central United States during the warm season (April–July). In this study, the skill of 3-hourly accumulated precipitation forecasts from a large sample of these convection-allowing simulations conducted during 2004–05 and 2007–08 is compared to that from operational North American Mesoscale (NAM) model forecasts using a neighborhood-based equitable threat score (ETS). Separate analyses were conducted for simulations run before and after the implementation in 2007 of positive-definite (PD) moisture transport for the NCAR-WRF simulations. The neighborhood-based ETS (denoted 〈ETS〉r) relaxes the criteria for “hits” (i.e., correct forecasts) by considering grid points within a specified radius r. It is shown that 〈ETS〉r is more useful than the traditional ETS because 〈ETS〉r can be used to diagnose differences in precipitation forecast skill between different models as a function of spatial scale, whereas the traditional ETS only considers the spatial scale of the verification grid. It was found that differences in 〈ETS〉r between NCAR-WRF and NAM generally increased with increasing r, with NCAR-WRF having higher scores. Examining time series of 〈ETS〉r for r = 100 and r = 0 km (which simply reduces to the “traditional” ETS), statistically significant differences between NCAR-WRF and NAM were found at many forecast lead times for 〈ETS〉100 but only a few times for 〈ETS〉0. Larger and more statistically significant differences occurred with the 2007–08 cases relative to the 2004–05 cases. Because of differences in model configurations and dominant large-scale weather regimes, a more controlled experiment would have been needed to diagnose the reason for the larger differences that occurred with the 2007–08 cases. Finally, a compositing technique was used to diagnose the differences in the spatial distribution of the forecasts. This technique implied westward displacement errors for NAM model forecasts in both sets of cases and in NCAR-WRF model forecasts for the 2007–08 cases. Generally, the results are encouraging because they imply that advantages in convection-allowing relative to convection-parameterizing simulations noted in recent studies are reflected in an objective neighborhood-based metric.

scholarly journals Impact of a Stochastic Kinetic Energy Backscatter Scheme on Warm Season Convection-Allowing Ensemble Forecasts

2016 ◽  
Vol 144 (5) ◽  
pp. 1887-1908 ◽  
Author(s):  
Jeffrey D. Duda ◽  
Xuguang Wang ◽  
Fanyou Kong ◽  
Ming Xue ◽  
Judith Berner
Keyword(s):  
Kinetic Energy ◽  
Wrf Model ◽  
Warm Season ◽  
Weather Research And Forecasting ◽  
Potential Utility ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Central United States ◽  
Surface Observations

The efficacy of a stochastic kinetic energy backscatter (SKEB) scheme to improve convection-allowing probabilistic forecasts was studied. While SKEB has been explored for coarse, convection-parameterizing models, studies of SKEB for convective scales are limited. Three ensembles were compared. The SKMP ensemble used mixed physics with the SKEB scheme, whereas the MP ensemble was configured identically but without using the SKEB scheme. The SK ensemble used the SKEB scheme with no physics diversity. The experiment covered May 2013 over the central United States on a 4-km Weather Research and Forecasting (WRF) Model domain. The SKEB scheme was successful in increasing the spread in all fields verified, especially mid- and upper-tropospheric fields. Additionally, the rmse of the ensemble mean was maintained or reduced, in some cases significantly. Rank histograms in the SKMP ensemble were flatter than those in the MP ensemble, indicating the SKEB scheme produces a less underdispersive forecast distribution. Some improvement was seen in probabilistic precipitation forecasts, particularly when examining Brier scores. Verification against surface observations agree with verification against Rapid Refresh (RAP) model analyses, showing that probabilistic forecasts for 2-m temperature, 2-m dewpoint, and 10-m winds were also improved using the SKEB scheme. The SK ensemble gave competitive forecasts for some fields. The SK ensemble had reduced spread compared to the MP ensemble at the surface due to the lack of physics diversity. These results suggest the potential utility of mixed physics plus the SKEB scheme in the design of convection-allowing ensemble forecasts.

scholarly journals Sensitivity of 0–12-h Warm-Season Precipitation Forecasts over the Central United States to Model Initialization

2012 ◽  
Vol 27 (4) ◽  
pp. 832-855 ◽  
Author(s):  
Juanzhen Sun ◽  
Stanley B. Trier ◽  
Qingnong Xiao ◽  
Morris L. Weisman ◽  
Hongli Wang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Case Studies ◽  
Large Scale ◽  
Initial Conditions ◽  
Warm Season ◽  
Radar Data ◽  
Short Wave ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Scale Model

Abstract Sensitivity of 0–12-h warm-season precipitation forecasts to atmospheric initial conditions, including those from different large-scale model analyses and from rapid cycled (RC) three-dimensional variational data assimilations (3DVAR) with and without radar data, is investigated for a 6-day period during the International H2O Project. Neighborhood-based precipitation verification is used to compare forecasts made with the Advanced Research core of the Weather Research and Forecasting Model (ARW-WRF). Three significant convective episodes are examined by comparing the precipitation patterns and locations from different forecast experiments. From two of these three case studies, causes for the success and failure of the RC data assimilation in improving forecast skill are shown. Results indicate that the use of higher-resolution analysis in the initialization, rapid update cycling via WRF 3DVAR data assimilation, and the additional assimilation of radar observations each play a role in shortening the period of the initial precipitation spinup as well as in placing storms closer to observations, thus improving precipitation forecast skill by up to 8–9 h. Impacts of data assimilation differ for forecasts initialized at 0000 and 1200 UTC. The case studies show that the pattern and location of the forecasted precipitation were noticeably improved with radar data assimilation for the two late afternoon cases that featured lines of convection driven by surface-based cold pools. In contrast, the RC 3DVAR, both with and without radar data, had negative impacts on convective forecasts for a case of morning elevated convection associated with a midlatitude short-wave trough.

scholarly journals Convection-permitting regional climate simulation of warm-season precipitation over Eastern China

2019 ◽  
Vol 54 (3-4) ◽  
pp. 1469-1489 ◽  
Author(s):  
Yuxing Yun ◽  
Changhai Liu ◽  
Yali Luo ◽  
Xudong Liang ◽  
Ling Huang ◽  
...  
Keyword(s):  
East Asia ◽  
Regional Climate ◽  
Eastern China ◽  
Precipitation Amount ◽  
Wrf Model ◽  
Warm Season ◽  
Climate Simulation ◽  
The Tibetan Plateau ◽  
Model Skill ◽  
Geographical Regions

AbstractConvection-permitting regional climate models have been shown to improve precipitation simulation in many aspects, such as the diurnal cycle, precipitation frequency, intensity and extremes in many studies over several geographical regions of the world, but their skill in reproducing the warm-season precipitation characteristics over the East Asia has not been robustly tested yet. Motivated by recent advances in computing power, model physics and high-resolution reanalysis, we use the convection-permitting weather research and forecasting (WRF) model configured with 3 km grid spacing to simulate the warm-season precipitation in eastern China for 10 seasons (2008–2017). The hourly 31-km-resolution ERA5 reanalysis data are used to provide initial and boundary conditions for the simulations. The objectives are (1) to evaluate the model skill in simulating warm-season precipitation climatology in the East Asian monsoon region, (2) to identify the promises and problems of the convection-permitting simulation, and (3) to investigate solutions for the model deficiencies. Results demonstrate that the 3-km-resolution WRF model reasonably reproduces the spatial characteristics of seasonal and sub-seasonal precipitation, the seasonal meridional migration associated with the summer monsoon activity, the diurnal variation phase and amplitude, and the propagating convection east of the Tibetan Plateau. The major deficiency is that the model overestimates precipitation amount, especially in the afternoon. Analysis and sensitivity experiments suggest that improved treatment of sub-grid cloud fraction and the aerosol effects may help to suppress the oft-reported high precipitation bias. These results provide useful guidance for improving the model skill at simulating warm-season precipitation in East Asia.

scholarly journals A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

2009 ◽  
Vol 24 (4) ◽  
pp. 1121-1140 ◽  
Author(s):  
Adam J. Clark ◽  
William A. Gallus ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Wrf Model ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Central United States ◽  
Statistical Consistency

Abstract An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 h. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ∼ 4 km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

scholarly journals A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea

2021 ◽  
Vol 14 (10) ◽  
pp. 6241-6255
Author(s):  
Sojung Park ◽  
Seon K. Park
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Subgrid Scale ◽  
Unknown Parameters ◽  
Quantitative Precipitation Forecast ◽  
Simulated Precipitation ◽  
Micro Genetic Algorithm ◽  
Physics Parameterizations

Abstract. One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating the subgrid-scale physical processes. For more accurate regional weather and climate prediction by improving physics parameterizations, it is important to optimize a combination of physics schemes and unknown parameters in NWP models. We have developed an interface system between a micro-genetic algorithm (µ-GA) and the WRF model for the combinatorial optimization of cumulus (CU), microphysics (MP), and planetary boundary layer (PBL) schemes in terms of quantitative precipitation forecast for heavy rainfall events in Korea. The µ-GA successfully improved simulated precipitation despite the nonlinear relationship among the physics schemes. During the evolution process, MP schemes control grid-resolving-scale precipitation, while CU and PBL schemes determine subgrid-scale precipitation. This study demonstrates that the combinatorial optimization of physics schemes in the WRF model is one possible solution to enhance the forecast skill of precipitation.

scholarly journals The application of LEPS technique for Quantitative Precipitation Forecast (QPF) in Southern Italy

2006 ◽  
Vol 7 ◽  
pp. 1-8 ◽  
Author(s):  
S. Federico ◽  
E. Avolio ◽  
C. Bellecci ◽  
M. Colacino
Keyword(s):  
Case Studies ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Computational Time ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Prediction System ◽  
Subjective Analysis ◽  
Quantitative Precipitation Forecast ◽  
The Impact

Abstract. This paper reports preliminary results of a Limited area model Ensemble Prediction System (LEPS), based on RAMS, for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time, in order to implement LEPS operational, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that forms the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12 km horizontal resolution. Hereafter this ensemble will be referred also as LEPS_12L30. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecast, LEPS_12L30 forecasts are compared to a lower resolution ensemble, based on RAMS that has 50 km horizontal resolution and 51 members, nested in each ECMWF-EPS member. Hereafter this ensemble will be also referred as LEPS_50L30. LEPS_12L30 and LEPS_50L30 results were compared subjectively for all case studies but, for brevity, results are reported for two "representative" cases only. Subjective analysis is based on ensemble-mean precipitation and probability maps. Moreover, a short summary of objective scores. Maps and scores are evaluated against reports of Calabria regional raingauges network. Results show better LEPS_12L30 performance compared to LEPS_50L30. This is obtained for all case studies selected and strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria, at least for set-ups and case studies selected in this work.

Does Increased Predicted Warm-Season Rainfall Indicate Enhanced Likelihood of Rain Occurrence?

10.1175/820.1 ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 1127-1135 ◽  
Author(s):  
William A. Gallus ◽  
Moti Segal
Keyword(s):  
Wrf Model ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
The United States ◽  
Precipitation Forecast ◽  
Convective System ◽  
Good Reliability ◽  
Relative Operating Characteristic ◽  
Probabilistic Forecasts ◽  
Test Sets

Abstract The likelihood of simulated rainfall above a specified threshold being observed is evaluated as a function of the amounts predicted by two mesoscale models. Evaluations are performed for 20 warm-season mesoscale convective system events over the upper Midwest of the United States. Simulations were performed using 10-km versions of the National Centers for Environmental Prediction Eta Model and the Weather Research and Forecasting (WRF) model, with two different convective parameterizations tested in both models. It was found that, despite large differences in the biases of these different models and configurations, a robust relationship was present whereby a substantial increase in the likelihood of observed rainfall exceeding a specified threshold occurred in areas where the model runs forecast higher rainfall amounts. Rainfall was found to be less likely to occur in those areas where the models indicated no rainfall than it was elsewhere in the domain; it was more likely to occur in those regions where rainfall was predicted, especially where the predicted rainfall amounts were largest. The probability of rainfall relative-operating-characteristic and relative-operating-level curves showed that probabilistic forecasts determined from quantitative precipitation forecast values had skill comparable to the skill obtained using more traditional methods in which probabilities are based on the fraction of ensemble members experiencing rainfall. When the entire sample of cases was broken into training and test sets, the probability forecasts of the test sets evidenced good reliability. The relationship noted should provide some additional guidelines to operational forecasters. The results imply that the tested models are better able to indicate the regions where atmospheric processes are most favorable for convective rainfall (where the models generate enhanced amounts) than they are able to predict accurately the rainfall amounts that will be observed.

scholarly journals The Dependence of QPF on the Choice of Boundary- and Surface-Layer Parameterization for a Lake-Effect Snowstorm

2015 ◽  
Vol 54 (6) ◽  
pp. 1177-1190 ◽  
Author(s):  
Robert Conrick ◽  
Heather Dawn Reeves ◽  
Shiyuan Zhong
Keyword(s):  
Surface Layer ◽  
Sensible Heat Flux ◽  
Moisture Flux ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Sensible Heat ◽  
Stability Function ◽  
Lake Effect ◽  
Quantitative Precipitation Forecast ◽  
Model Layer

AbstractSix forecasts of a lake-effect-snow event off Lake Erie were conducted using the Weather Research and Forecasting Model to determine how the quantitative precipitation forecast (QPF) was affected when the boundary- and surface-layer parameterization schemes were changed. These forecasts showed strong variability, with differences in liquid-equivalent precipitation maxima in excess of 20 mm over a 6-h period. The quasi-normal scale elimination (QNSE) schemes produced the highest accumulations, and the Mellor–Yamada–Nakanishi–Niino (MYNN) schemes produced the lowest. Differences in precipitation were primarily due to different sensible heat flux FH and moisture flux FQ off the lake, with lower FH and FQ in MYNN leading to comparatively weak low-level instability and, consequently, reduced ascent and production of hydrometeors. The different FH and FQ were found to have two causes. In QNSE, the higher FH and FQ were due to the decision to use a Prandtl number PR of 0.72 (all other schemes use a PR of 1). In MYNN, the lower FH and FQ were due to the manner in which the similarity stability function for heat ψh is functionally dependent on the temperature gradient between the surface and the lowest model layer. It is not known what assumptions are more accurate for environments that are typical for lake-effect snow, but comparisons with available observations and Rapid-Update-Cycle analyses indicated that MYNN had the most accurate results.

scholarly journals A Features-Based Assessment of the Evolution of Warm Season Precipitation Forecasts from the HRRR Model over Three Years of Development

2017 ◽  
Vol 32 (5) ◽  
pp. 1841-1856 ◽  
Author(s):  
Janice L. Bytheway ◽  
Christian D. Kummerow ◽  
Curtis Alexander
Keyword(s):  
Data Assimilation ◽  
Model Development ◽  
Wrf Model ◽  
Warm Season ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Central Plains ◽  
Convective Initiation ◽  
Precipitation Forecasting ◽  
Data Assimilation System

Abstract The High Resolution Rapid Refresh (HRRR) model has been the National Weather Service’s (NWS) operational rapid update model since 2014. The HRRR has undergone continual development, including updates to the Weather Research and Forecasting (WRF) Model core, the data assimilation system, and the various physics packages in order to better represent atmospheric processes, with updated operational versions of the model being implemented approximately every spring. Given the model’s intent for use in convective precipitation forecasting, it is of interest to examine how forecasts of warm season precipitation have changed as a result of the continued model upgrades. A features-based assessment is performed on the first 6 h of HRRR quantitative precipitation forecasts (QPFs) from the 2013, 2014, and 2015 versions of the model over the U.S. central plains in an effort to understand how specific aspects of QPF performance have evolved as a result of continued model development. Significant bias changes were found with respect to precipitation intensity. Model upgrades that increased boundary layer stability and reduced the strength of the latent heating perturbations in the data assimilation were found to reduce southward biases in convective initiation, reduce the tendency for the model to overestimate heavy rainfall, and improve the representation of convective initiation.

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