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 Prediction of the 14 June 2010 Oklahoma City Extreme Precipitation and Flooding Event in a Multiphysics Multi-Initial-Conditions Storm-Scale Ensemble Forecasting System

2016 ◽  
Vol 31 (4) ◽  
pp. 1215-1246 ◽  
Author(s):  
Nathan Dahl ◽  
Ming Xue
Keyword(s):  
Initial Conditions ◽  
Stage Iv ◽  
Radar Data ◽  
Ensemble Forecast ◽  
Forecast Skill ◽  
Oklahoma City ◽  
Precipitation Forecast ◽  
Cold Pool ◽  
Statistical Correlations ◽  
Outflow Boundary

Abstract Prolonged heavy rainfall produced widespread flooding in the Oklahoma City area early on 14 June 2010. This event was poorly predicted by operational models; however, it was skillfully predicted by the Storm-Scale Ensemble Forecast produced by the Center for Analysis and Prediction of Storms as part of the Hazardous Weather Testbed 2010 Spring Experiment. In this study, the quantitative precipitation forecast skill of ensemble members is assessed and ranked using a neighborhood-based threat score calculated against the stage IV precipitation data, and Oklahoma Mesonet observations are used to evaluate the forecast skill for surface conditions. Statistical correlations between skill metrics and qualitative comparisons of relevant features for higher- and lower-ranked members are used to identify important processes. The results demonstrate that the development of a cold pool from previous convection and the movement and orientation of the associated outflow boundary played dominant roles in the event. Without assimilated radar data from this earlier convection, the modeled cold pool was too weak and too slow to develop. Furthermore, forecast skill was sensitive to the choice of microphysics parameterization; members that used the Thompson scheme produced initial cold pools that propagated too slowly, substantially increasing errors in the timing and placement of later precipitation. The results also suggest important roles played by finescale, transient features in the period of outflow boundary stalling and reorientation associated with the heaviest rainfall. The unlikelihood of a deterministic forecast reliably predicting these features highlights the benefit of using convection-allowing/convection-resolving ensemble forecast methods for events of this kind.

scholarly journals The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System

2015 ◽  
Vol 144 (1) ◽  
pp. 193-212 ◽  
Author(s):  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
M. K. Yau
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Radar Data ◽  
Ensemble Forecasting ◽  
Precipitation Forecast ◽  
Quantitative Precipitation Forecast ◽  
Spatial Coverage ◽  
Forecasting System ◽  
Forecast Time ◽  
Radar Data Assimilation

Abstract This paper analyzes the case-to-case variability of the predictability of precipitation by a storm-scale ensemble forecasting (SSEF) system. Relationships are sought between ensemble spread and quantitative precipitation forecast (QPF) skill, and the characteristics of an event, such as the strength of the quasigeostrophic forcing for ascent, the presence of convective equilibrium, and the spatial extent of the precipitation system. It is found that most of the case-to-case variability of predictability is explained by the spatial coverage of the system. The relationship between convection and large-scale forcing seems to affect predictability mostly during the afternoon hours. While the relationships are weak for the entire dataset, two distinct types of cases are identified: widespread and diurnally forced cases. The loss of predictability at small scales, the effect of the radar data assimilation, and the comparison between forecasts from the SSEF and Lagrangian persistence forecasts are analyzed separately for these two types of cases. Despite overall predictability being better than average for the widespread cases, the loss of predictability with forecast time and spatial scale is just as rapid as for the other cases. For the diurnally forced cases, the radar data assimilation causes larger differences between the precipitation fields corresponding to the assimilating and nonassimilating members than for the widespread cases. However, the effect of radar data assimilation on QPF skill is similar for both types of cases. Also, for the diurnal cases, the models with radar data assimilation outperform very rapidly (after 2 h) the Lagrangian persistence forecasts.

scholarly journals Impact of Storm-Scale Lightning Data Assimilation on WRF-ARW Precipitation Forecasts during the 2013 Warm Season over the Contiguous United States

2015 ◽  
Vol 143 (3) ◽  
pp. 757-777 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Adam J. Clark ◽  
Edward R. Mansell ◽  
Donald R. MacGorman ◽  
Scott R. Dembek ◽  
...  
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Large Scale ◽  
Warm Season ◽  
The United States ◽  
Scale Model ◽  
Large Scale Model ◽  
Forecast Models ◽  
Total Lightning ◽  
Assimilation Scheme

Abstract This work evaluates the performance of a recently developed cloud-scale lightning data assimilation technique implemented within the Weather Research and Forecasting Model running at convection-allowing scales (4-km grid spacing). Data provided by the Earth Networks Total Lightning Network for the contiguous United States (CONUS) were assimilated in real time over 67 days spanning the 2013 warm season (May–July). The lightning data were assimilated during the first 2 h of simulations each day. Bias-corrected, neighborhood-based, equitable threat scores (BC-ETSs) were the chief metric used to quantify the skill of the forecasts utilizing this assimilation scheme. Owing to inferior observational data quality over mountainous terrain, this evaluation focused on the eastern two-thirds of the United States. During the first 3 h following the assimilation (i.e., 3-h forecasts), all the simulations suffered from a high wet bias in forecasted accumulated precipitation (APCP), particularly for the lightning assimilation run (LIGHT). Forecasts produced by LIGHT, however, had a noticeable, statistically significant (α = 0.05) improvement over those by the control run (CTRL) up to 6 h into the forecast with BC-ETS differences often exceeding 0.4. This improvement was seen independently of the APCP threshold (ranging from 2.5 to 50 mm) and the neighborhood radius (ranging from 0 to 40 km) selected. Past 6 h of the forecast, the APCP fields from LIGHT progressively converged to that of CTRL probably due to the longer-term evolution being bounded by the large-scale model environment. Thus, this computationally inexpensive lightning assimilation scheme shows considerable promise for routinely improving short-term (≤6 h) forecasts of high-impact weather by convection-allowing forecast models.

scholarly journals Variational data assimilation with superparameterization

2015 ◽  
Vol 2 (2) ◽  
pp. 513-536 ◽  
Author(s):  
I. Grooms ◽  
Y. Lee
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Climate Models ◽  
Low Cost ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Scale Model ◽  
Small Scale ◽  
New Applications ◽  
Lorenz 96

Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Yi}, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.

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.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

2006 ◽  
Vol 63 (11) ◽  
pp. 2813-2830 ◽  
Author(s):  
Roger Marchand ◽  
Nathaniel Beagley ◽  
Sandra E. Thompson ◽  
Thomas P. Ackerman ◽  
David M. Schultz
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate ◽  
Stable State ◽  
Local Scale ◽  
Global Climate Models ◽  
Scale Model ◽  
Synoptic Scale ◽  
Cloud Properties

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

scholarly journals ESR and EISCAT observations of the response of the cusp and cleft to IMF orientation changes

2000 ◽  
Vol 18 (9) ◽  
pp. 1009-1026 ◽  
Author(s):  
I. W. McCrea ◽  
M. Lockwood ◽  
J. Moen ◽  
F. Pitout ◽  
P. Eglitis ◽  
...  
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Plasma Temperature ◽  
Radar Data ◽  
Auroral Oval ◽  
Hf Radar ◽  
High Electron ◽  
Vhf Radar ◽  
Ionosphere Plasma ◽  
Main Cusp

Abstract. We report observations of the cusp/cleft ionosphere made on December 16th 1998 by the EISCAT (European incoherent scatter) VHF radar at Tromsø and the EISCAT Svalbard radar (ESR). We compare them with observations of the dayside auroral luminosity, as seen by meridian scanning photometers at Ny Ålesund and of HF radar backscatter, as observed by the CUTLASS radar. We study the response to an interval of about one hour when the interplanetary magnetic field (IMF), monitored by the WIND and ACE spacecraft, was southward. The cusp/cleft aurora is shown to correspond to a spatially extended region of elevated electron temperatures in the VHF radar data. Initial conditions were characterised by a northward-directed IMF and cusp/cleft aurora poleward of the ESR. A strong southward turning then occurred, causing an equatorward motion of the cusp/cleft aurora. Within the equatorward expanding, southward-IMF cusp/cleft, the ESR observed structured and elevated plasma densities and ion and electron temperatures. Cleft ion fountain upflows were seen in association with elevated ion temperatures and rapid eastward convection, consistent with the magnetic curvature force on newly opened field lines for the observed negative IMF By. Subsequently, the ESR beam remained immediately poleward of the main cusp/cleft and a sequence of poleward-moving auroral transients passed over it. After the last of these, the ESR was in the polar cap and the radar observations were characterised by extremely low ionospheric densities and downward field-aligned flows. The IMF then turned northward again and the auroral oval contracted such that the ESR moved back into the cusp/cleft region. For the poleward-retreating, northward-IMF cusp/cleft, the convection flows were slower, upflows were weaker and the electron density and temperature enhancements were less structured. Following the northward turning, the bands of high electron temperature and cusp/cleft aurora bifurcated, consistent with both subsolar and lobe reconnection taking place simultaneously. The present paper describes the large-scale behaviour of the ionosphere during this interval, as observed by a powerful combination of instruments. Two companion papers, by Lockwood et al. (2000) and Thorolfsson et al. (2000), both in this issue, describe the detailed behaviour of the poleward-moving transients observed during the interval of southward Bz, and explain their morphology in the context of previous theoretical work.Key words: Ionosphere (ionosphere - magnetosphere interactions; auroral ionosphere; plasma temperature and density)

scholarly journals Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

2018 ◽  
Vol 33 (2) ◽  
pp. 599-607 ◽  
Author(s):  
John R. Lawson ◽  
John S. Kain ◽  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Dustan M. Wheatley ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Radar Data ◽  
Model Systems ◽  
Deterministic Systems ◽  
Forecast Lead Time ◽  
The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

Sign in / Sign up

Export Citation Format

Share Document