Quantitative Precipitation Forecasts Research Articles

Lightning data assimilation with comprehensively nudging water contents at cloud-resolving scale using WRF model

A new lightning data assimilation (LDA) scheme comprehensively nudging water contents in the WRF model is developed at cloud-resolving scale, which takes the dynamical and thermodynamic conditions into consideration and nudges the low-level water vapor and graupel mass within the mixed-phase region according to the detected total lightning flash rate and model environments (here named C18). The main nudging functions of the LDA scheme in this work are modified based on the work of Fierro et al. (2012, hereafter F12) and Qie et al. (2014, hereafter Q14). To evaluate the application of LDA in high-impact weather simulations and quantitative precipitation forecasts, the three LDA schemes, including F12, Q14 and C18, are tested and compared for two severe squall line cases occurred over the Beijing metropolitan region (BMR). Benefiting from assimilation of lightning data, the simulated storm structure and surface cold pool are improved and closer to the observations for all the three schemes than the control run without LDA. For the F12 scheme, although the simulated thunderstorms are characterized by wide spread stratiform clouds and relatively weaker cold pool intensity and coverage, the positive impacts on both storm evolution and precipitation endures most prominently. For the Q14 scheme, intense downdrafts lead to the strongest cold pool and earlier dissipation of storms comparing with the observations. For the C18 scheme, increased graupel mass affect the cold-cloud processes leading to an earlier onset of rainfall, and the simulated surface cold pool is in better agreement with the observations. Profiting from more moisture added into the lower layers in C18, the storm develops more intensively toward higher altitude and sustains its development over a longer period after LDA, resulting in the best quantitative precipitation forecasts consequently.

High-resolution modeling study of an isolated convective storm over Seoul Metropolitan area

The ability of a high-resolution (500 m) Weather Research and Forecasting (WRF) model to simulate an isolated convective precipitation event over Seoul metropolitan area on August 16, 2015 was investigated. To understand the effects of micro- and mesoscale forcing on the initiation of convective rainfall under large-scale conditions, we performed sensitivity tests using different initial times. Despite simulating the same case, the quantitative precipitation forecast and the timing of moist convection varied widely among the experiments. Mesoscale features such as outflow and low-level convergence are different in location and intensity among the experiments. When assimilation of surface observations and radar data was performed, the simulation reproduced the low-level convergence and, hence, the location and amount of rainfall reasonably well within the first 6 h of simulation period. The timing differences of convective rainfall among the experiments were examined in terms of the atmospheric boundary layer (ABL) growth. Rapid growth of the ABL enabled moist convection to occur early in the presence of an outflow. An overestimated maximum ABL height by the model also led to earlier collapse of the ABL as compared to observations, which contributed to a reduction of convective available potential energy over the urban area in late afternoon. The results of this study demonstrate that accurate simulation of ABL growth is important for predicting the timing and intensity of isolated convective storms.

Short-Term Precipitation Forecasting Rolling Update Correction Technology Based on Optimal Fusion Correction

In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.

A Comparison of Two Methods for Bias Correcting Precipitation Skill Scores

Abstract A fair comparison of quantitative precipitation forecast (QPF) products from multiple forecast sources using performance metrics based on a 2 × 2 contingency table with assessment of statistical significance of differences requires accounting for differing frequency biases to which the performance metrics are sensitive. A simple approach to address differing frequency biases modifies the 2 × 2 contingency table values using a mathematical assumption that determines the change in hit rate when the frequency bias is adjusted to unity. Another approach uses quantile mapping to remove the frequency bias of the QPFs by matching the frequency distribution of each QPF to the frequency distribution of the verifying analysis or points. If these two methods consistently yield the same result for assessing the statistical significance of differences between two QPF forecast sources when accounting for bias differences, then verification software can apply the simpler approach and existing 2 × 2 contingency tables can be used for statistical significance computations without recovering the original QPF and verifying data required for the bias removal approach. However, this study provides evidence for continued application and wider adoption of the bias removal approach.

Assessing the predictability of WRF model for heavy rain by cold air associated with the easterly wind at high-level patterns over mid-central Vietnam

Abstract: In this study, the authors assessed the possibility of predicting precipitation due to cold air associated with the easterly wind at the high level in the WRF model with 2 days lead time for the Mid-Central Vietnam region. The results show that in the 24-hour forecasts lead time should use medium rainfall threshold (16-50 mm/day) and heavy rain (50-100 mm/day) to referent for quantitative precipitation forecast and rainfall area; for 48-hour forecast lead time should choose moderate rainfall threshold. The threshold of over 100 mm, the results of all of forecast lead time is not good, almost unpredictable. The results of this study can help forecasters have more information for forecasting rain due to cold air associated easterly wind at the high level for the Mid-Central Vietnam region.

Spatial verification of high-resolution ensemble precipitation forecasts using local wavelet spectra

The purpose of this study is to perform verification of the structural characteristics of high-resolution spatial forecasts without relying on an object identification algorithm. To this end, a wavelet approach developed for image texture analysis is applied to an ensemble of high-resolution quantitative precipitation forecasts. The forecasts are verified against estimates from a high-resolution regional reanalysis with a similar model version. The wavelet approach estimates an averaged wavelet spectrum for each spatial field of the ensemble forecasts and the reanalysis, thereby removing all information on the localization of precipitation and investigating solely the overall structure of forecasts and reanalysis. In order to assess skill using a multivariate score, an additional reduction of dimensionality is needed. This is performed using singular vectors from a linear discriminant analysis as it favors data compression in the direction where the ensemble is most discriminating. We discuss implications of this strategy, show that the averaged wavelet spectra give valuable information on forecast performance. The skill difference between a so-called perfect forecast using for verification a member of the ensemble, and the non-perfect forecast using the reanalysis points to significant deficiencies of the forecast ensemble. Overall, the discriminating power solely based on global spectral information is remarkable, and the COSMO-DE-EPS is a quite good forecast ensemble with respect to the reanalysis.

Assessment of Met Office Unified Model (UM) quantitative precipitation forecasts during the Indian summer monsoon: Contiguous Rain Area (CRA) approach

The operational medium range rainfall forecasts of the Met Office Unified Model (UM) are evaluated over India using the Contiguous Rainfall Area (CRA) verification technique. In the CRA method, forecast and observed weather systems (defined by a user-specified rain threshold) are objectively matched to estimate location, volume, and pattern errors. In this study, UM rainfall forecasts from nine (2007–2015) Indian monsoon seasons are evaluated against $$0.5^{\circ }\times 0.5^{\circ }$$ IMD–NCMRWF gridded observed rainfall over India $$(6.5^{\circ }{-}38.5^{\circ }\hbox {N}, 66.5^{\circ }{-}100.5^{\circ }\hbox {E})$$ . The model forecasts show a wet bias due to excessive number of rainy days particularly of low amounts $$({<}1\,\hbox {mm}\,\hbox {d}^{-1})$$ . Verification scores consistently suggest good skill the forecasts at threshold of $$10\,\hbox {mm}\,\hbox {d}^{-1}$$ , while moderate (poor) skill at thresholds of $${<}20\,\hbox {mm}\,\hbox {d}^{-1}\,({<}40\,\hbox {mm}\,\hbox {d}^{-1})$$ . Spatial verification of rainfall forecasts is carried out for 10, 20, 40 and $$80\,\hbox {mm}\,\hbox {d}^{-1}$$ CRA thresholds for four sub-regions namely (i) northwest (NW), (ii) southwest (SW), (iii) eastern (E), and (iv) northeast (NE) sub-region. Over the SW sub-region, the forecasts tend to underestimate rain intensity. In the SW region, the forecast events tended to be displaced to the west and southwest of the observed position on an average by about $$1^{\circ }$$ distance. Over eastern India (E) forecasts of light (heavy) rainfall events, like $$10\,\hbox {mm}\,\hbox {d}^{-1}$$ (20 and $$40\,\hbox {mm}\,\hbox {d}^{-1}$$ ) tend to be displaced to the south on an average by about $$1^{\circ }$$ (southeast by $$1{-}2^{\circ }$$ ). In all four regions, the relative contribution to total error due to displacement increases with increasing CRA threshold. These findings can be useful for forecasters and for model developers with regard to the model systematic errors associated with the monsoon rainfall over different parts of India.

Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

AbstractShort‐term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud‐Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short‐term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short‐Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root‐mean‐square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root‐mean‐square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short‐term forecasting over compared methods, and has the potential to be implemented globally as an alternative short‐term forecast product.

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.

Probabilistic Precipitation Forecast Postprocessing Using Quantile Mapping and Rank-Weighted Best-Member Dressing

Abstract Hamill et al. described a multimodel ensemble precipitation postprocessing algorithm that is used operationally by the U.S. National Weather Service (NWS). This article describes further changes that produce improved, reliable, and skillful probabilistic quantitative precipitation forecasts (PQPFs) for single or multimodel prediction systems. For multimodel systems, final probabilities are produced through the linear combination of PQPFs from the constituent models. The new methodology is applied to each prediction system. Prior to adjustment of the forecasts, parametric cumulative distribution functions (CDFs) of model and analyzed climatologies are generated using the previous 60 days’ forecasts and analyses and supplemental locations. The CDFs, which can be stored with minimal disk space, are then used for quantile mapping to correct state-dependent bias for each member. In this stage, the ensemble is also enlarged using a stencil of forecast values from the 5 × 5 surrounding grid points. Different weights and dressing distributions are assigned to the sorted, quantile-mapped members, with generally larger weights for outlying members and broader dressing distributions for members with heavier precipitation. Probability distributions are generated from the weighted sum of the dressing distributions. The NWS Global Ensemble Forecast System (GEFS), the Canadian Meteorological Centre (CMC) global ensemble, and the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast data are postprocessed for April–June 2016. Single prediction system postprocessed forecasts are generally reliable and skillful. Multimodel PQPFs are roughly as skillful as the ECMWF system alone. Postprocessed guidance was generally more skillful than guidance using the Gamma distribution approach of Scheuerer and Hamill, with coefficients generated from data pooled across the United States.

Data assimilation of radar reflectivity volumes in a LETKF scheme

Abstract. Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

Physical-based rainfall-triggered shallow landslide forecasting

This study demonstrates the applications of two physical-based early warning methods for rainfall-induced shallow landslide and compare their relative performance. One method is rainfall threshold-based method and the other method is by real-time simulation. The former establishes landslide threshold in advance using 50 historical rainfall events, in conjunction with physical-based rainfall-triggered shallow landslide model, to evaluate the stability of a concerned slope. Quantitative precipitation estimation (QPE) for nowcast is used in the rainfall threshold-based method for landslide predictions. The latter method also applies rainfall-triggered landslide model in real-time simulation by feeding QPE and quantitative precipitation forecast (QPF) of specified lead-time. Both methods are integrated into an early warning system (e.g. Delft-FEWS) for real-time nowcast and/or forecast purposes. The two shallow landslide early warning methods are applied to a slope in the vicinity of a highway section in Taiwan. Comparisons between the two methods are made to evaluate their performance using rainfall data from three past typhoon events. Despite of some discrepancies found in the results, both methods can predict landslide quite consistently. Without sufficient number of actual landslide records for model validation, the true accuracy of both landslide early warning methods cannot be assessed. However, the consistency of predicted landslide occurrence times during three historical typhoon events in the case study indicated that they could be viable for providing good supportive information for decision-making in landslide hazard mitigation.

Analysis of atmospheric thermodynamics using the R package aiRthermo

The publicly available R package aiRthermo is presented in this study, which allows the user to process information relative to atmospheric thermodynamics, ranging from calculating the density of dry or moist air and converting data between moisture indices to processing a full sounding, obtaining factors such as the convective available potential energy, additional instability indices, or adiabatic evolutions of particles. The package also provides the possibility to present information using customisable Stüve diagrams. Many of the functions are written inside a C extension to ensure that the computations are fast. The results of applying this package to five years of real soundings measured over the Iberian Peninsula are also presented as an example. The package considerably extends the capabilities of R to process atmospheric soundings or model results. This will be useful for many practical environmental forecasting applications at different scales, such as statistical downscaling for climate analysis, quantitative precipitation forecasting (particularly precipitation extremes), diagnosing storms, flash floods, and lightning, and in aviation and other fields where computing atmospheric convection and its related parameters is important.

Evaluation and Benchmarking of Operational Short-Range Ensemble Mean and Median Streamflow Forecasts for the Ohio River Basin

Abstract This study presents findings from a real-time forecast experiment that compares legacy deterministic hydrologic stage forecasts to ensemble mean and median stage forecasts from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS). The NOAA/NWS Ohio River Forecast Center (OHRFC) area of responsibility defines the experimental region. Real-time forecasts from subbasins at 54 forecast point locations, ranging in drainage area, geographic location within the Ohio River valley, and watershed response time serve as the basis for analyses. In the experiment, operational hydrologic forecasts, with a 24-h quantitative precipitation forecast (QPF) and forecast temperatures, are compared to MMEFS hydrologic ensemble mean and median forecasts, with model forcings from the NOAA/NWS National Centers for Environmental Prediction (NCEP) North American Ensemble Forecast System (NAEFS), over the period from 30 November 2010 through 24 May 2012. Experiments indicate that MMEFS ensemble mean and median forecasts exhibit lower errors beginning at about lead time 90 h when forecasts at all locations are aggregated. With fast response basins that peak at ≤24 h, ensemble mean and median forecasts exhibit lower errors much earlier, beginning at about lead time 36 h, which suggests the viability of using MMEFS ensemble forecasts as an alternative to OHRFC legacy forecasts. Analyses show that ensemble median forecasts generally exhibit smaller errors than ensemble mean forecasts for all stage ranges. Verification results suggest that OHRFC MMEFS NAEFS ensemble forecasts are reasonable, but needed improvements are identified.

On Different Microphysical Pathways to Convective Rainfall

ABSTRACTThe Convective Precipitation Experiment (COPE) documented the dynamical and microphysical evolution of convection in southwestern England for testing and improving quantitative precipitation forecasting. A strong warm rain process was hypothesized to produce graupel quickly, initiating ice production by rime splintering earlier to increase graupel production and, ultimately, produce heavy rainfall. Here, convection observed on two subsequent days (2 and 3 August 2013) is used to test this hypothesis and illustrate how environmental factors may alter the microphysical progression. The vertical wind shear and cloud droplet number concentrations on 2 August were 2 times those observed on 3 August. Convection on both days produced comparable maximum radar-estimated rain rates, but in situ microphysical measurements indicated much less ice in the clouds on 2 August, despite having maximum cloud tops that were nearly 2 km higher than on 3 August. Idealized 3D numerical simulations of the convection in their respective environments suggest that the relative importance of particular microphysical processes differed. Higher (lower) cloud droplet number concentrations slow (accelerate) the warm rain process as expected, which in turn slows (accelerates) graupel formation. Rime splintering can explain the abundance of ice observed on 3 August, but it was hampered by strong vertical wind shear on 2 August. In the model, the additional ice produced by rime splintering was ineffective in enhancing surface rainfall; strong updrafts on both days lofted supercooled raindrops well above the 0°C level where they froze to become graupel. The results illustrate the complexity of dynamical–microphysical interactions in producing convective rainfall and highlight unresolved issues in understanding and modeling the competing microphysical processes.

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

As a preliminary attempt to cope with the low predictability of heavy rainfall over southern China in the pre‐summer rainy season, an experimental convection‐permitting ensemble prediction system (GM‐CPEPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is developed. GM‐CPEPS produces 12 h forecasts at 0.03° horizontal resolution based on 16 perturbed members. Perturbations from downscaling, ensemble of data assimilation, time‐lagged scheme and topography are combined to generate the initial perturbations. Sea‐surface temperature is perturbed and a combination of downscaling and balanced random perturbations is used to perturb the lateral boundary conditions. Stochastically perturbed parametrization tendencies, multi‐physics, and perturbed parameters are all implemented.In this study, GM‐CPEPS was verified over a 15‐day period during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. It was indicated that GM‐CPEPS provided estimates of forecast uncertainty that are comparable to some international peers. Compared with the control forecasts (DET), some deterministic guidance, including the forecast distribution with 90th percentile, probability‐matched mean, and linear combination of both (NPM), showed advantages in forecasting moderate and heavy rainfall; and the optimal‐member technique was superior in reducing bias. Probabilistic guidance demonstrated the value over DET in detecting potential threats of severe weather, with both the optimal‐probability and neighbourhood‐probability technique leading to improvements in predicting lighter rainfall. Two cases were used to display the deterministic and probabilistic guidance intuitively and to illustrate corresponding advantages and drawbacks.

Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

The Setup of the MesoVICT Project

AbstractRecent advancements in numerical weather prediction (NWP) and the enhancement of model resolution have created the need for more robust and informative verification methods. In response to these needs, a plethora of spatial verification approaches have been developed in the past two decades. A spatial verification method intercomparison was established in 2007 with the aim of gaining a better understanding of the abilities of the new spatial verification methods to diagnose different types of forecast errors. The project focused on prescribed errors for quantitative precipitation forecasts over the central United States. The intercomparison led to a classification of spatial verification methods and a cataloging of their diagnostic capabilities, providing useful guidance to end users, model developers, and verification scientists. A decade later, NWP systems have continued to increase in resolution, including advances in high-resolution ensembles. This article describes the setup of a second phase of the verification intercomparison, called the Mesoscale Verification Intercomparison over Complex Terrain (MesoVICT). MesoVICT focuses on the application, capability, and enhancement of spatial verification methods to deterministic and ensemble forecasts of precipitation, wind, and temperature over complex terrain. Importantly, this phase also explores the issue of analysis uncertainty through the use of an ensemble of meteorological analyses.

Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

High-Resolution QPF Uncertainty and Its Implications for Flood Prediction: A Case Study for the Eastern Iowa Flood of 2016

Abstract This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling–Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1–3-h lead times and starts increasing with 4–6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction.