Quantitative Precipitation Forecast Skill Research Articles

Classification of Warm-Season Precipitation in High-Resolution Rapid Refresh (HRRR) Model Forecasts over the Contiguous United States

Abstract This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and nonequilibrium convection in different parts of the contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium), while locally forced, nonequilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment time-scale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and nonequilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability. Significance Statement An objective classification metric that can delineate a wide range of forecasts into distinct scenarios can serve as a valuable tool. This study represents a pioneering effort in utilizing the convective adjustment time scale to identify the climatological frequency of warm-season precipitation under varying levels of synoptic forcing in different parts of the contiguous United States (CONUS). The results demonstrate that the convective adjustment time scale is a robust metric for categorizing precipitation events and establishing a direct link to their predictability. Overall, this study provides a valuable framework for future studies focused on the CONUS domain, offering guidance on how to employ the convective adjustment time scale to classify weather regimes and explore the influence of environmental conditions on predictability of convection.

Improving AHI Radiance Assimilation over Land with the Surface Skin Temperature Constrained by Station Observations and Its Impact for Quantitative Precipitation Forecasts

Abstract In this study, a new way to assimilate clear-sky Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) observations over land is investigated for improving quantitative precipitation forecasts (QPFs) in eastern China. To alleviate problems arising from inaccurate surface temperature in radiance simulations, surface station observations of land surface skin temperature (LSST) together with conventional and AMSU-A observations are assimilated to improve AHI surface-sensitive TB simulations of the Community Radiative Transfer Model (CRTM) before AHI data assimilation. First, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVar) system is updated with the additional control variable of surface temperature and its background error covariances. Second, surface temperature and emissivity sensitivity checks are designed for the quality control of the surface-sensitive AHI channels. Finally, the impacts of a two-time data assimilation strategy are assessed for a local convection rainfall case and a synoptic-scale precipitation case. The experiment in which AHI data are assimilated after assimilating LSST data (ExpL2) outperforms the traditional experiment in which the LSST is not updated (ExpL) in terms of its 24-h QPF skill score. A better description of atmospheric instability and moisture convergence forcing is obtained in ExpL2 than in ExpL. Both experiments show additional low-level temperature and humidity adjustments compared to the experiment that does not assimilate AHI surface-sensitive channels (ExpNL). Lower AHI TB simulation biases are found in the ExpL2 experiment, which improve the analyzed field and subsequent QPFs. The results in this study suggest the importance of proper utilization of LSST observations for AHI surface-sensitive TB assimilations over land.

Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model

Urban extreme rainfall events (UEREs) are one of the weather hazards where modelers continue to struggle for accurate advance prediction despite recent advances in forecasting techniques. Bangalore, one of the prominent IT hubs in India, witnessed many urban flash floods in the recent past; a number of such events happened on 14 and 15 of August 2017, where overnight heavy rains brought several parts of the city to a standstill. In this study, the skill of sub-kilometer resolution forecasts from Weather Research and Forecasting (WRF) model in simulating these UEREs is examined. This study also compared the model performance for forecasts initialized from different forecast cycles in a day. Our examination for two UERE cases showed that forecast initialized at 0600 UTC yielded best results in terms of forecasting spatial distribution and intensity of rainfall. Our analysis also indicated that increasing model resolution further in convection-permitting resolution (< 3 km) has only marginal impact on spatial distribution of rainfall while model skill in simulating rainfall intensity slightly improved with increasing resolution. As expected, simulation by not deploying a cumulus scheme at cloud-resolving resolution do not make a significant difference in spatial distribution; however, inclusion of cumulus scheme improved quantitative precipitation forecast skill. The inferences from this study are useful for modelers working on improving operational forecast skill of UEREs using mesoscale model.

Assimilation of no-precipitation observations from Doppler radar with 4DVar and its impact on summertime convective event prediction

No-precipitation reflectivity observations from Doppler radar contain valuable information on areas lacking precipitation; however, they are often ignored in four-dimensional variational (4DVar) radar data assimilation (DA). This study incorporated a neighborhood-based scheme to assimilate no-precipitation observations as a mechanism to suppress spurious convection. The impact of the scheme on convective forecasting using 4DVar was evaluated by comparing the performance of experiments with and without assimilation (ExpCTL) using eight diverse storm cases that occurred over the central United States during summer 2016. Three no-precipitation assimilation experiments with different neighborhood radiuses of 10 (ExpR10), 30 (ExpR30), and 50 km (ExpR50) were conducted to examine the sensitivity of the scheme to neighborhood size. Results indicated that all the no-precipitation assimilation experiments significantly improved quantitative precipitation forecast skill with large reduction of the bias and the false alarm ratio from ExpCTL, as well as improving representation of the intensity and coverage of the precipitation. The horizontal wind, temperature, and water vapor were also improved, especially the latter. The scheme was found sensitive to neighborhood size and greater benefit was found in ExpR30 in comparison with ExpR10 and ExpR50. Analysis revealed that ExpR30 reduced low-level cooling and mid-level warming corresponding to decreased water vapor in areas of overpredicted and false precipitation, and it was more effective in conserving the total water content balance during cycled radar DA. The findings of this study could provide reference information for assimilation of no-precipitation observations into the Weather Research and Forecasting model using 4DVar, which would be valuable for severe weather prediction.

Impact of track forecast error on tropical cyclone quantitative precipitation forecasts over the coastal region of China

Qualitative and quantitative assessments of the impact of track forecast error on tropical cyclone (TC) quantitative precipitation forecasts (QPFs) are presented in this study. The original and track-error-corrected QPFs extracted from the Global Forecast System (GFS) of 52 TCs over the coastal region of China during 2015–2017 were compared with the Global Precipitation Measurement (GPM) observations. The track forecast error was corrected by shifting the QPF according to the difference between the forecasted and observed TC centers. Qualitative evaluations revealed that large track forecast error tends to cause severe under-forecast of total rainfall volume over a region. Quantitative assessments showed that the impact of track forecast error generally increases with track forecast error up to 500 km. The rapid decline of QPF skill with lead time is mainly attributed to the fast-growing track forecast error. Track forecast error has large impact on moderate to heavy rainfall and little impact on extremely heavy rainfall. Based on the assessments, a new operational post-processing method was developed to reduce the impact of track forecast error on QPFs from numerical models. The new technique was able to improve the TC QPFs in general and especially at longer lead times.

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.

Comparison of the Performance of Hybrid ETKF-3DVAR and 3DVAR Data Assimilation Systems on Short-Range Forecasts during Indian Summer Monsoon Season in a Limited-Area Model

The impact of deploying a flow-dependent ensemble error covariance in Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation (DA) system is examined for short-range rainfall forecasts during an Indian summer monsoon season. The flow-dependent background error covariance (BEC) is generated using a 50-member ensemble, which is further updated using the ensemble transform Kalman filter (ETKF). Assimilation is performed using a Hybrid variational-ensemble (“Hybrid”) and traditional 3DVAR DA system during the 4 weeks of July 2013. The forecasted wind, temperature, and rainfall from the assimilation experiments are verified against corresponding observations. The results indicate that the flow-dependent ensemble background error covariance in 3DVAR has systematically improved the forecasted wind and temperature when compared to the traditional 3DVAR. Similarly, rainfall forecast skill is superior in the Hybrid experiments relative to that of 3DVAR. Convection-permitting resolution rainfall forecast is validated against 746 telemetric rain gauge observations over the state of Karnataka. The Hybrid experiments show higher quantitative precipitation forecast skill than 3DVAR, particularly towards the later stages of data assimilation cycling. Spatially, the 3DVAR experiment shows a dry bias over the upper peninsular regions and a slight wet bias over the central and the northern Indian regions, while the magnitude of such wet and dry biases is smaller in forecasts from Hybrid analysis. Additionally, the westerly wind over the peninsular Indian landmass analyzed by 3DVAR is considerably weaker than that analyzed by the Hybrid experiments. This is proposed as a possible reason for the reduced dry bias in rainfall forecasts over the Indian landmass in Hybrid versus 3DVAR experiments.

Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm

Inclusion of cloud processes is essential for precipitation prediction with a numerical weather prediction model. However, convective parameterization contains numerous parameters whose values are in large uncertainties. In particular, it is still not clear how the parameters of a sub-grid-scale convection scheme can be modified to improve high-resolution precipitation prediction. To address these issues, a micro-genetic (micro-GA) algorithm is coupled to the Kain-Fritsch (KF) convective parameterization scheme (CPS) in the WRF to improve the quantitative precipitation forecast (QPF). The optimization focuses on two parameters in the KF scheme: the convective time scale and the conversion rate. The optimizing process is controlled by the micro-GA using a QPF skill score as the fitness function. Two heavy rainfall events related to typhoons that made landfall over the south-east coastal region of China are selected, and for each case the parameter values are adjusted to achieve the best QPF skill. Significant improvements in QPF are evident with an increase in the average equitable threat score (ETS) by 5.8% for the first case, and by 18.4% for the second case. The results demonstrate that the micro-GAKF coupling system is effective in optimizing the parameter values, which affect the applicability of CPS in a high-resolution model, and therefore improves the rainfall prediction in both ETS and spatial distribution.

A New Metric to Diagnose Precipitation Distribution in Transitioning Tropical Cyclones

A new coupled dynamic and thermodynamic metric is developed based on the Eady Moist Baroclinic Growth Rate (EMBGR), to discriminate between left-of-track (LOT) and right-of-track (ROT) precipitation distributions in transitioning tropical cyclones (TCs). LOT events pose a major flood risk even when a TC tracks along a coastline or just offshore, as flash flooding can occur hundreds of kilometers inland from the cyclone center. The EMBGR can improve human-produced quantitative precipitation forecasts (QPF) because it is dependent on relatively well-forecast large-scale mass fields. The ability of the EMBGR to identify precipitation distribution is first explored in a case study of TC Matthew (2016), using reanalysis and numerical model forecasts. Subsequently, a composite analysis of 36 years (1979–2014) of United States landfalling TCs using reanalysis data shows that the EMBGR is an effective discriminator between LOT and ROT distributions. The utility of the EMBGR is quantified using a pattern correlation analysis for both TC Matthew and the composites. Finally, a conceptual schematic is developed for LOT cases so that forecasters can most effectively utilize the EMBGR to improve human QPF skill during transitioning TCs.

Precipitation Forecast Experiments Using the Weather Research and Forecasting (WRF) Model at Gray-Zone Resolutions

Abstract In the Weather Research and Forecasting (WRF) community, a standard model setup at a grid size smaller than 5 km excludes cumulus parameterization (CP), although it is unclear how to determine a cutoff grid size where convection permitting can be assumed adequate. Also, efforts to improve high-resolution precipitation forecasts in the range of 1–10 km (the so-called gray zone for parameterized precipitation physics) have recently been made. In this study, we attempt to statistically evaluate the skill of a gray-zone CP with a focus on the quantitative precipitation forecast (QPF) in the summertime. A WRF Model simulation with the gray-zone simplified Arakawa–Schubert (GSAS) CP at 3-km spatial resolution over East Asia is evaluated for the summer of 2013 and compared with the results from a conventional setup without CP. A statistical evaluation of the 3-month simulations shows that the GSAS demonstrates a typical distribution of the QPF skill, with high (low) scores and bias in the light (heavy) precipitation category. The WRF without CP seriously suppresses light precipitation events, but its skill for heavier categories is better. Meanwhile, a new set of precipitation data, which is simply averaged precipitation from the two simulations, demonstrates the best skill in all precipitation categories. Bearing in mind that high-resolution QPF requires essential challenges in model components, along with complexity in precipitating convection mechanisms over geographically different regions, this proposed method can serve as an alternative for improving the QPF for practical usage.

Equatorial Waves and the Skill of NCEP and ECMWF Numerical Weather Prediction Systems

Despite decades of research on the role of moist convective processes in large-scale tropical dynamics, tropical forecast skill in operational models is still deficient when compared to the extratropics, even at short lead times. Here we compare tropical and Northern Hemisphere (NH) forecast skill for quantitative precipitation forecasts (QPFs) in the NCEP Global Forecast System (GFS) and ECMWF Integrated Forecast System (IFS) during January 2015–March 2016. Results reveal that, in general, initial conditions are reasonably well estimated in both forecast systems, as indicated by relatively good skill scores for the 6–24-h forecasts. However, overall, tropical QPF forecasts in both systems are not considered useful by typical metrics much beyond 4 days. To quantify the relationship between QPF and dynamical skill, space–time spectra and coherence of rainfall and divergence fields are calculated. It is shown that while tropical variability is too weak in both models, the IFS is more skillful in propagating tropical waves for longer lead times. In agreement with past studies demonstrating that extratropical skill is partially drawn from the tropics, a comparison of daily skill in the tropics versus NH suggests that in both models NH forecast skill at lead times beyond day 3 is enhanced by tropical skill in the first couple of days. As shown in previous work, this study indicates that the differences in physics used in each system, in particular, how moist convective processes are coupled to the large-scale flow through these parameterizations, appear as a major source of tropical forecast errors.

An evaluation of the WRF Mei‐yu rainfall forecasts in Taiwan, 2008–2010: differences in elevation and sub‐regions

ABSTRACTThis study explores differences in the performance of the Mei‐yu quantitative precipitation forecast (QPF) in Taiwan by use of the Weather Research and Forecasting (WRF) model in continuation of an earlier study, with particular detailed focus on those among three elevation ranges and four sub‐regions at different stages of the season and within the diurnal cycle. Against data from about 400 rain gauges, both subjective and objective verifications are performed to complement each other. The Mei‐yu QPF's skill is shown to vary with evaluation periods (every half‐month) within the season, with elevation (categorized as plain (0–250 m), slope (0.25–1 km) and mountain (&gt;1 km) areas), and also with sub‐region (northern, central, southern and eastern Taiwan). The diurnal cycle of rainfall produced by the model is strong over the mountains, but too weak over the plains. Overall, the model exhibits the highest skill in southern and central Taiwan, where more rain occurs due to forced uplift of the prevailing southwesterly flow over the windward slopes (mainly during the first half of June). In general, the more rainfall there is, the better the model performs (with higher skill) during the Mei‐yu season.

Tuning of length‐scale and observation‐error for radar data assimilation using four dimensional variational (4D‐Var) method

AbstractThe effects of tuning of length‐scale and observation‐error on heavy rainfall forecasts are investigated. Length scale and observation error are tuned based on observation minus background (O − B) covariances and theoretically expected cost function values, respectively. Tuned length scale and observation error are applied to radar data assimilation using the Four Dimensional Variational (4D‐Var) method. Length‐scale tuning leads to improved Quantitative Precipitation Forecast (QPF) skill for heavy precipitation, better analyses, and reduced errors of wind, temperature, humidity, and hydrometeor forecasts. The effects of observation‐error tuning are not as significant as those of length‐scale tuning, and they are limited to improvements in QPF skill. This is because tuned observation errors are close to pre‐assumed values. Proper tuning of length‐scale and observation‐error is essential for radar data assimilation using the 4D‐Var method.

Satellite radiance data assimilation for rainfall prediction in Java Region

This study examined the influence of satellite radiance data assimilation for predicting two days of heavy rainfall in the Java region. The first case occurred from 22 to 23 on January 2015 while the second case occurred from 1 to 2 on February 2015. The analysis examined before and after data assimilation in the two cases study. The Global Forecast System (GFS) data were used as initial condition which was assimilated with several data such as surface observation data, radiance data from AMSUA sensor, radiance data from HIRS sensor, and radiance data from MHS sensor. Weather Research and Forecasting Data Assimilation (WRFDA) is a tool which is used in this study for assimilating process with Three Dimensional Variation (3D-Var) method. The Quantitative Precipitation Forecast (QPF) skill was used to evaluate influence data assimilation for rainfall prediction. The result of the study obtained different rainfall prediction with different data assimilation. In general, the surface observation data assimilation has lower QPF skill than the satellite radiance data assimilation. Even thought radiance data assimilation has slightly contribution on rainfall prediction, but it gave better accuracy on rainfall prediction for two heavy rainfall cases.

Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts

Since the launch of the NOAA‐15 satellite in 1998, the observations from microwave temperature and humidity sounders have been routinely disseminated to user communities through two separate data streams. In the Advanced Microwave Sounding Unit‐A (AMSU‐A) data stream, brightness temperatures in 15 channels are available primarily for profiling atmospheric temperature from the Earth's surface to the low stratosphere. In the Advanced Microwave Sounding Unit‐B (AMSU‐B) or Microwave Humidity Sounder (MHS) data stream, the brightness temperatures in five channels are included for sounding water vapour in the low troposphere. Assimilation of microwave radiance data in numerical weather prediction systems has also been carried out with AMSU‐A and AMSU‐B (MHS) data in two separate data streams. A new approach is to combine AMSU‐A and MHS radiances into one data stream for their assimilation. The National Centers for Environmental Prediction Gridpoint Statistical Interpolation analysis system and the Advanced Research Weather Research and Forecast model are employed for testing the impacts of the combined datasets. It is shown that the spatial collocation between MHS and AMSU‐A fields of view in the one data stream experiment allows for an improved quality control of MHS data, especially over the conditions where the liquid‐phase clouds are dominant. As a result, a closer fit of analyses to AMSU‐A and MHS observations is obtained, especially for AMSU‐A surface‐sensitive channels. The quantitative precipitation forecast skill is improved over a 10‐day period when Hurricane Isaac made landfall.

The Brazilian Global Atmospheric Model (BAM): Performance for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and Horizontal Resolution

Abstract This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

Impact of Assimilating Wind Profiling Radar Observations on Convection-Permitting Quantitative Precipitation Forecasts during SCMREX

AbstractTo improve the prediction of heavy rainfall in southern China during the prerainy season, horizontal wind data from wind profiling radars (WPRs) were assimilated in the partial-cycle data assimilation (DA) system based on a three-dimensional variational method. The analyses from the DA system are used as initial conditions for the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model. The impact of assimilating WPR data on the quantitative precipitation forecast (QPF) in southern China was evaluated over the period of the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014, by comparing the results of a control experiment with WPR data assimilated and a denial experiment without WPR data. The positive impact of WPR DA was significant on the forecasts of atmospheric variables in the vertical and diagnostic fields at the surface, especially those of surface wind fields in the 0–6-h range. The inclusion of WPR data also improved the QPF skill of light and heavy rainfall throughout the 12-h forecast period by reducing the predicted spurious precipitation (thereby alleviating overestimations and false alarms), with the largest improvement in 6-h heavy rainfall forecasts. WPR DA considerably alleviated the spinup problem, remarkably improving the QPF of heavy rainfall (especially extreme rainfall). The improved representation of wind and moisture at lower levels in the analyses due to WPR DA was the physical cause of the QPF improvement, as is illustrated using a case study.

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

AbstractProlonged 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.

Incorporating Diagnosed Intercept Parameters and the Graupel Category within the ARPS Cloud Analysis System for the Initialization of Double-Moment Microphysics: Testing with a Squall Line over South China

Abstract In this study, a new set of reflectivity equations are introduced into the Advanced Regional Prediction System (ARPS) cloud analysis system. This set of equations incorporates double-moment microphysics information in the analysis by adopting a set of diagnostic relationships between the intercept parameters and the corresponding mass mixing ratios. A reflectivity- and temperature-based graupel classification scheme is also implemented according to a hydrometeor identification (HID) diagram. A squall line that occurred on 23 April 2007 over southern China containing a pronounced trailing stratiform precipitation region is used as a test case to evaluate the impacts of the enhanced cloud analysis scheme. The results show that using the enhanced cloud analysis scheme is able to better capture the characteristics of the squall line in the forecast. The predicted squall line exhibits a wider stratiform region and a more clearly defined transition zone between the leading convection and the trailing stratiform precipitation region agreeing better with observations in general, when using the enhanced cloud analysis together with the two-moment microphysics scheme. The quantitative precipitation forecast skill score is also improved.

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

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.