Precipitation Forecast Skill Research Articles

Assessment of Subseasonal-to-Seasonal (S2S) Precipitation Forecast Skill for Reservoir Operation in the Yaque Del Norte River, Dominican Republic

Assessment of Subseasonal-to-Seasonal (S2S) Precipitation Forecast Skill for Reservoir Operation in the Yaque Del Norte River, Dominican Republic

The Improvement of Short-Term Quantitative Precipitation Forecast in Mountainous Areas by the Assimilation of Meteorological State Variables Retrieved by Multiple Doppler Radar Data

Abstract This study presents a sequential procedure formulated by combining a multiple-Doppler radar wind synthesis technique with a thermodynamic retrieval method, which can be applied to retrieve the three-dimensional wind, pressure, temperature, rainwater mixing ratio, and moisture over complex terrain. The retrieved meteorological state variables are utilized to reinitialize a high-resolution numerical model, which then carries out time integration using four different microphysical (MP) schemes, including the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. It is found that through this procedure, the short-term quantitative precipitation forecast (QPF) skill of a numerical model over mountainous areas can be significantly improved up to 6 h. The moisture field plays a crucial role in producing the correct rainfall forecast. Since no specific microphysical scheme outperforms the others, a combination of various rainfall scenarios forecasted by different MP schemes is suggested in order to provide a stable and reliable rainfall forecast. This work also demonstrates that, with the proposed approach, radar data from only two volume scans are sufficient to improve the rainfall forecasts. This is because the unobserved meteorological state variables are instantaneously retrieved and directly used to reinitialize the model, thereby the model spinup time can be effectively shortened.

Impacts of Initial Condition Perturbation Blending in 10- and 40-Member Convection-Allowing Ensemble Forecasts

Abstract A series of convection-allowing 36-h ensemble forecasts during the 2018 spring season are used to better understand the impacts of ensemble configuration and blending different sources of initial condition (IC) perturbation. Ten- and forty-member ensemble configurations are initialized with the multiscale IC perturbations generated as a product of convective-scale data assimilation (MULTI) and initialized with the MULTI IC perturbations blended with IC perturbations downscaled from coarser-resolution ensembles (BLEND). The forecast performance of both precipitation and nonprecipitation variables is consistently improved by the larger ensemble size. The benefit of the larger ensemble is largely, but not entirely, due to compensating for underdispersion in the fixed-physics ensemble configuration. A consistent improvement in precipitation forecast skill results from blending in the 10-member ensemble configuration, corresponding to a reduction in the ensemble calibration error (i.e., reliability component of Brier score). In the 40-member ensemble configuration, the advantage of blending is limited to the ∼18–22-h lead times at all precipitation thresholds and the ∼35–36-h lead times at the lowest threshold, both corresponding to an improved resolution component of the Brier score. The advantage of blending in the 40-member ensemble during the diurnal convection maximum of ∼18–22-h lead times is primarily due to cases with relatively weak synoptic-scale forcing, while advantages at later lead times beyond ∼30-h lead time are most prominent on cases with relatively strong synoptic-scale forcing. The impacts of blending and ensemble configuration on forecasts of nonprecipitation variables are generally consistent with the impacts on the precipitation forecasts.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In this study, we develop an ensemble algorithm based on the feature-oriented mean (FM) suitable for spatially discrete variables in precipitation ensembles. This method can adjust the locations of ensemble precipitation fields to reduce the location-related deviations among ensemble members, ultimately enhancing the ensemble mean forecast skill for TC precipitation. To evaluate the feasibility of the FM in TC precipitation ensemble forecasting, 18 landing TC cases in China from 2019 to 2021 were selected for validation. For precipitation forecasts of the landing TCs with a varying leading time, we conducted a comprehensive quantitative evaluation and comparison of the precipitation forecast skills of the FM and arithmetic mean (AM) algorithms. The results indicate that the field adjustment algorithm in the FM can effectively align with the TC precipitation structure and the location of the ensemble mean, reducing the spatial divergence among precipitation fields. The FM method demonstrates superior performance in the equitable threat score, probability of detection, and false alarm ratio compared with the AM, exhibiting an overall improvement of around 10%. Furthermore, the FM ensemble mean shows a higher pattern of the correlation coefficient with observations and has a smaller root mean square error than the AM ensemble mean, signifying that the FM method can better preserve the characteristics of the precipitation structure. Additionally, an object-based diagnostic evaluation method was used to verify forecast results, and the results suggest that the attribute distribution of FM forecast objects more closely resembles that of observed precipitation objects (including the area, longitudinal and latitudinal centroid locations, axis angle, and aspect ratio).

An Assessment of Dropsonde Sampling Strategies for Atmospheric River Reconnaissance

Abstract During a 6-day intensive observing period in January 2021, Atmospheric River Reconnaissance (AR Recon) aircraft sampled a series of atmospheric rivers (ARs) over the northeastern Pacific that caused heavy precipitation over coastal California and the Sierra Nevada. Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of research flight frequency and spatial resolution of dropsondes on model analyses and forecasts. Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times > 1 day. Both reduced mission frequency and reduced dropsonde horizontal resolution degrade forecast skill. On the other hand, experiments that assimilated only G-IV data and experiments that assimilated both G-IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the additional information provided by G-IV data is necessary for improving forecast skill. Although this is a case study, the 6-day period studied encompassed multiple AR events that are representative of typical AR behavior. Therefore, the results indicate that future operational AR Recon missions incorporate daily mission or back-to-back flights, maintain current dropsonde spacing, support high-resolution data transfer capacity on the C-130s, and utilize G-IV aircraft in addition to C-130s.

Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach

Accurate and reliable precipitation predictions made by dynamical forecast models could provide crucial information for human socioeconomic activities by enabling hydrologic forecasts at the Subseasonal-to-Seasonal (S2S) timescale. To utilize available S2S precipitation predictions for hydrologic forecasts, post-processing techniques have been applied to adapt the raw S2S precipitation to local watersheds. However, conventional statistical-based post-processing techniques are more focused on correcting the forecast bias, but rather limited in improving the predictive skill of available S2S precipitation forecasts. In this study, we combine the Random Forest classifiers (RF) with the Bias Correction and Spatial Disaggregation (BCSD) to adapt the 10-member ensemble precipitation forecast from the NASA Goddard Earth Observing System model version 5 (GEOS5) at 4 watersheds located in the NCEI South climate region of the United States. The adapted S2S precipitation is further applied for streamflow forecast by forcing a classical lumped hydrologic model. The performance of S2S precipitation as well as the corresponding streamflow predictions are benchmarked with the randomly resampled precipitation and the corresponding Ensemble Streamflow Prediction (ESP) framework-generated streamflow predictions. Evaluation statistics of Kling-Gupta Efficiency (KGE), Continuous Ranked Probability Skill Score (CRPSS), Reliability, Resolution, and Sharpness are employed to evaluate the predictive skill of precipitation and streamflow both deterministically and probabilistically. Our results indicate that dynamical S2S precipitation after forecast adaptation leads to consistently higher deterministic skill over ESP at all forecast lead times and across study watersheds. However, at longer forecast lead times beyond 10–15 days, S2S precipitation with a limited ensemble size does not present higher probabilistic skill than ESP. Our results shows that the joint application of RF and BCSD improves the predictive skill of the raw S2S precipitation at study watersheds in contrast to BCSD. Further, the added predictive skill of S2S precipitation brought by RF propagates into streamflow predictions, predominantly at longer forecast lead times exceeding 10 days. Overall, our results highlight the potential success of future work to apply other data-driven approaches to adapt the raw precipitation to local watersheds for more accurate and reliable streamflow forecasts at the S2S timescale.

Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson aerosol-aware scheme in WRF: Statistical analysis and significant threat score improvements in polluted condition during June to August 2018

The North China Plain (NCP) is one of the most polluted areas in China, and the impacts of such high-levels of aerosols on clouds and precipitation remain an interesting issue with great uncertainties. From the perspective of regional numerical weather prediction, how to consider the role of aerosols is still a controversial issue considering the balance between the complexities/uncertainties of the model (aerosol-radiation-cloud interactions) and computational costs. To evaluate the reliability of the Thompson aerosol-aware schemes and promote operational applications in the Weather Research and Forecasting model (WRF), summer precipitation forecast with three experiments that considered no aerosol-radiation-cloud interactions (Thomp), aerosol-cloud interactions only (ThompAI) and full aerosol-radiation-cloud interactions (ThompAD) from June to August 2018 were carried out. Detailed statistics of the 3-h and 24-h cumulative precipitation skills were calculated for two groups, which included the entire summer season and selected polluted-precipitation events (26 days) only, were analyzed. The results showed that the overall Threat Scores (TS) for the 26 polluted-precipitation events were lower than those of the other group (the whole summer season)for either Thomp, ThompAI or ThompAD, which revealed that the forecasting abilities for polluted-precipitation cases in the model were obviously lower than those for the nonpolluted precipitation cases, indicating deficiencies in parameterizing the special conditions on polluted days. Nevertheless, for both groups, after using the aerosol-aware Thompson schemes (either ThompAI or ThompAD), the TS scores for most magnitudes (0.1, 1, 5, 10, and 25 mm) were significantly increased compared to the original Thomp experiment, with the exception being for precipitation amounts >50 mm. Meanwhile the BIAS of each magnitude also increased for both ThompAI and ThompAD due to the spatial increase in areas with precipitation compared to the original Thomp experiment. The TS improvements were more significant in the ThompAD experiment than in the ThompAI experiment. Compared with the original Thomp experiment, the ThompAD improvement ratios for the 24-h cumulative precipitation TSs were 3.01%, 4.91%, 2.05%, 13.58%, 7.80% for 0.1 mm, 1 mm, 5 mm, 10 mm, 25 mm precipitation magnitudes respectively; and the improvements were significantly higher when the statistics window was changed from 24-h to 3-h, with 3-h cumulative precipitation TS improvements reaching 10.23%, 5.09%, 11.50%, 17.4%, 14.30%, respectively, indicating the impacts that considering aerosol-radiation-cloud interactions in changing the precipitation patterns (fall zone and amounts) in the model were more effective for 3-h windows compared to 24-h windows. Case studies of selected light-rain, medium-scale rain and heavy rain events further revealed the positive impacts of the Thompson aerosol-aware schemes, and the precipitated areas were much closer to the observations. The reason why ThompAD did not show obvious advantages for large-scale precipitation (50 mm magnitude) may be due to the complex relationship involving aerosol-cold cloud interactions in the model and the randomness caused by inadequate an individual number of cases. The statistics for positive precipitation forecast skill improvements for the NCP region provide confidence in the application of the Thompson aerosol-aware scheme in the WRF model in the future.

Short-term forecast large-scale error characteristics and their relationship with precipitation forecast skill under two rainfall regimes

Although precipitation forecast skill has improved significantly in recent decades, both location and intensity of precipitation forecast based on kilometric-scale numerical weather prediction (NWP) models still contain errors. The importance of large-scale error in NWP has been recognized, but characteristics of large-scale error under different rainfall regimes and the influence of large-scale error on precipitation forecast skill remain unclear. In this regard, we focused on the Yangtze-Huaihe River Basin (YHRB) and used an operational forecast dataset covering the summer seasons of 2018 and 2019 obtained from the Anhui Meteorology Observatory to investigate large-scale error characteristics and their relationship with precipitation errors under two rainfall regimes.Large-scale error characteristics were measured by root-mean-square errors (RMSE), relative RMSE (RRMSE), and large-scale spatial pattern correlation (LSCC). The large-scale RMSEs were highly correlated with the rainfall intensity and area, while the large-scale RRMSE and LSCC can be used to describe precipitation forecast skill for rainfall events under the two rainfall regimes. In general, the precipitation forecast for strongly forced rainfall events performed better than that for weakly forced rainfall events. Under strong forcing conditions, the majority of rainfall events were concentrated in the intervals of smaller precipitation errors with smaller RRMSEs and higher LSCCs, and the precipitation forecast skill was weakly related to the large-scale RRMSE, LSCC and the initial large-scale variance contribution (LVC). Under weak forcing conditions, most of the rainfall events had a larger precipitation error and the frequency of location error was higher than that of intensity error. The low precipitation forecast skill and large forecast uncertainties for weakly forced rainfall events were strongly related to the large RRMSEs and low LSCCs in the geopotential height in the middle-upper troposphere and meridional wind component (V) in the lower troposphere. In particular, a strong negative correlation was found between the initial LVC and the large-scale RRMSE under weak forcing. This suggests that large-scale information could play a more important role in precipitation forecast for weakly forced rainfall events, and could potentially be used to indicate the magnitude of large-scale forecast errors in short-term forecasts under weak forcing conditions.

A statistical–dynamical approach for probabilistic prediction of sub-seasonal precipitation anomalies over 17 hydroclimatic regions in China

Abstract. In this study, we develop a spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method to improve probabilistic sub-seasonal precipitation forecast skill over 17 hydroclimatic regions in China. The calibration model is established by post-processing ECMWF raw forecasts using the Bayesian joint probability (BJP) approach. The bridging models are built using large-scale atmospheric intraseasonal predictors, including zonal wind at 200 hPa (U200) and 850 hPa (U850); an outgoing longwave radiation anomaly (OLRA); and geopotential height at 200 hPa (H200), 500 hPa (H500), and 850 hPa (H850) defined by the STP method. The calibration model and the bridging models are then merged through the Bayesian modelling averaging (BMA) method. Our results indicate that the forecast skill of the calibration model is higher compared to bridging models when the lead time is within 5–10 d. The U200- and OLRA-based bridging models outperform the calibration model in certain months and certain regions. The BMA-merged forecasts take advantage of both calibration models and bridging models. Meanwhile, the BMA-merged forecasts also show high reliability at longer lead times. However, some improvements to reliability are still needed at shorter lead times. These findings demonstrate the great potential to combine dynamical models and statistical models in improving sub-seasonal precipitation forecasts.

Ensemble-based analysis of heavy rainfall–snowfall associated with mesoscale precipitation bands within an extratropical cyclone over northeastern China

This paper investigates the key synoptic-scale factors that affected the forecasting of mesoscale rainfall and snowfall and their associated uncertainties in a heavy rain–snow event in northeastern China on 18–20 November 2020, using ensemble-based sensitivity analysis based on global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. The heavy precipitation event was attributed to an extratropical cyclone and experienced two stages, with the snowfall stage having a better precipitation forecast skill than the rainfall stage. The mesoscale rainfall and snowfall were caused by a mesoscale rainband over Liaoning Province and two mesoscale snowbands over Heilongjiang Province, respectively, and they showed some differences with respect to their forecast skill and related key synoptic-scale factors contributing to the precipitation centers. The precipitation amount in the two different stages was correlated significantly with the midlevel trough and sensitive to the location and intensity of the low-level vortex (surface cyclone), and particularly the low-level jets and the associated water vapor transport. However, some differences were confirmed in the two different stages: the weaker midlevel trough and accompanying weaker low-level temperature trough in the rainfall stage were related to increased precipitation because the midlevel trough was far away from the control area, while the stronger midlevel trough and accompanying stronger low-level temperature trough were associated with increased precipitation in the snowfall stage. In addition to the synoptic-scale low-level jet (SLLJ), the precipitation in the rainfall stage was also affected by a boundary layer jet (BLJ) over the ocean, while only SLLJs were present in the snowfall stage. The uncertainty of the precipitation forecast was derived mainly from the uncertainty in the strength and location of the SLLJs and BLJ. Notably, the intensity of northeasterly winds west of the low-level vortex may affect the predictability of heavy snowfall.

Assessment of subseasonal-to-seasonal (S2S) ensemble extreme precipitation forecast skill over Europe

Abstract. Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal-to-seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to assess the forecast skill of rare events, here extreme precipitation, in S2S forecasts, using a metric specifically designed for extremes. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyze daily events that are locally and spatially aggregated, as well as 7 d extreme-event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g., temperature extremes or river discharge.

A Multiscale Four-Dimensional Variational Data Assimilation Scheme: A Squall-Line Case Study

Abstract This study presents a multiscale four-dimensional variational data assimilation (MS-4DVar) scheme that aims to assimilate multiscale information from conventional and radar observations. The MS-4DVar scheme separately assimilates conventional and radar data in different outer loop iterations of an incremental 4DVar with varied resolutions in the tangent linear and adjoint models (TLM/ADM) and time window lengths in the 4DVar. The MS-4DVar scheme was evaluated through a series of single observation tests and several cycled assimilation and forecasting experiments for a real squall-line case. Our results indicated that different TLM/ADM resolutions and time window lengths applied to the conventional and radar observations improved the multiscale analysis. In addition, the MS-4DVar scheme was more efficient than the common 4DVar because of the low-resolution TLM/ADM used for conventional data and the shortened time window length for radar data. Verification of the squall-line forecasts suggested that the MS-4DVar scheme improved the hourly accumulated precipitation and radar reflectivity forecast skills and reduced the forecast errors of both large-scale environmental and convective-scale states. Further diagnosis revealed that the improvement of precipitation forecast skill was attributable to the stronger cold pool, deeper saturated water vapor layer, and stronger updraft of the simulated squall-line system, as well as a more favorable convective environment.

The WRF-Based Incremental Analysis Updates and Its Implementation in an Hourly Cycling Data Assimilation System

Abstract An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data are included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spinup and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.

The Impact of Tropical SST Biases on the S2S Precipitation Forecast Skill over the Contiguous United States in the UFS Global Coupled Model

Abstract This work investigates the impact of tropical sea surface temperature (SST) biases on the Subseasonal to Seasonal Prediction project (S2S) precipitation forecast skill over the contiguous United States (CONUS) in the Unified Forecast System (UFS) coupled model Prototype 6. Boreal summer (June–September) and winter (December–March) for 2011–18 were analyzed. The impact of tropical west Pacific (WP) and tropical North Atlantic (TNA) warm SST biases is evaluated using multivariate linear regression analysis. A warm SST bias over the WP influences the CONUS precipitation remotely through a Rossby wave train in both seasons. During boreal winter, a warm SST bias over the TNA partly affects the magnitude of the North Atlantic subtropical high (NASH)’s center, which in the reforecasts is weaker than in reanalysis. The weaker NASH favors an enhanced moisture transport from the Gulf of Mexico, leading to increased precipitation over the Southeast United States. Compared to reanalysis, during boreal summer, the NASH’s center is also weaker and in addition, its position is displaced to the northeast. The displacement further affects the CONUS summer precipitation. The SST biases over the two tropical regions and their impacts become stronger as the forecast lead increases from week 1 to 4. These tropical biases explain up to 10% of the CONUS precipitation biases on the S2S time scale.

Flow Dependence of Medium-Range Precipitation Forecast Skill over California

Abstract This study employs a long time series (1997–2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3–15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November–March 1981–2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release. Significance Statement This study examines the degree to which the medium-range (out to ∼2-week lead time) precipitation forecast skill over California depends on the large-scale atmospheric flow regime over the North Pacific. An evaluation of retrospective model forecasts from ECMWF for 1997–2017 reveals that the skill tends to be considerably lower in regimes featuring a wavy, “blocked” North Pacific jet stream than in regimes featuring a west–east-oriented jet stream. This difference in skill relates to a tendency for forecasts of blocked regimes to exhibit significantly larger errors than forecasts of zonal regimes. The results could aid forecasters by increasing situational awareness and informing the interpretation and application of model forecasts for precipitation affecting California.

Subseasonal Ensemble Prediction of Flash Droughts over China

Abstract Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. Significance Statement Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

To clarify the precipitation forecast skills of climate forecast operations in the flood season in Liaoning Province of China, this study examines the forecast accuracies of China’s national and provincial operational climate prediction products and the self-developed objective prediction methods and climate model products by Shenyang Regional Climate Center (SRCC) in the flood season in Liaoning. Furthermore, the forecast accuracies of the main influencing factors on the precipitation in the flood season of Liaoning are assessed. The results show that the SRCC objective methods have a relatively high accuracy. The European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecast initialized at the sub-nearest time has the best performance in June. The National Climate Center (NCC) Climate System Model sub-seasonal forecast initialized at the sub-nearest time, and the ECMWF seasonal and sub-seasonal forecasts initialized at the nearest time, perform the best in July. The NCC sub-seasonal forecast initialized at the sub-nearest time has the best performance in August. For the accuracy of the SRCC objective method, the more significant the equatorial Middle East Pacific sea surface temperature (SST) anomaly is, the higher the evaluation score of the dynamic–analogue correction method is. The more significant the North Atlantic SST tripole is, the higher the score of the hybrid downscaling method is. For the forecast accuracy of the main influencing factors of precipitation, the tropical Atlantic SST and the north–south anti-phase SST in the northwest Pacific can well predict the locations of the southern vortex and the northern vortex in early summer, respectively. The warm (clod) SST in China offshore has a good forecast performance on the weak (strong) southerly wind in midsummer in Northeast China. The accuracy of using the SST in the Nino 1+2 areas to predict the north–south location of the western Pacific subtropical high is better than that of using Kuroshio SST. The accuracy of predicting northward-moving typhoons from July to September by using the SST in the west-wind-drift area is better than using the SST in the Nino 3 area. The above conclusions are of great significance for improving the short-term climate prediction in Liaoning.

Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity

AbstractThis paper examines the empirical relationship between the Madden–Julian oscillation (MJO), the quasi‐biennial oscillation (QBO), and atmospheric river (AR) activity and precipitation in California on subseasonal time scales. We introduce an experimental forecast tool that uses observed anomaly patterns during a 38 yr period to predict the probability of above‐ and below‐normal AR activity and precipitation at lead times of 1–6 weeks based on the phase and amplitude of the MJO and QBO. The hindcast prediction skill of probabilistic AR activity and precipitation forecasts is evaluated for Northern, Central, and Southern California, as well as two sets of smaller geographical domains. These smaller domains are more relevant for water resource management and allow us to investigate the sensitivity of prediction skill to domain size. Consistent with previous studies, our results demonstrate that subseasonal AR activity and precipitation in California are strongly modulated by the MJO and QBO. The anomaly patterns of AR activity and precipitation vary considerably throughout the cool season, with a tendency toward below‐normal AR activity and precipitation during easterly QBO and above‐normal AR activity and precipitation during westerly QBO in JFM. The opposite patterns are generally observed in OND, but the anomaly signals are weaker and less coherent for AR activity. Certain combinations of MJO phase, QBO phase, lag time, and season yield notably higher skill scores, reinforcing the notion of “windows of opportunity” for skillful subseasonal‐to‐seasonal predictions. In California, these forecasts of opportunity are predominantly associated with easterly QBO in JFM and FMA.

Impact of hydrometeor control variables in a convective‐scale 3DEnVar data assimilation scheme

AbstractInitialization of condensate water variables for convective‐scale weather forecasting has been a topic of active research for the last few years. In this article, we present the addition of hydrometeor fields as control variables of a three‐dimensional ensemble variational scheme (3DEnVar) for the cloud‐resolving model AROME‐France. Even without any direct assimilation of hydrometeor observations, analysis increments of hydrometeors can be produced via covariances with observed variables in the ensemble‐derived background‐error covariance matrix. Cycled forecast–analysis experiments in near‐operational conditions have been performed over a three‐month summer period. Three configurations are compared: (i) a control experiment without hydrometeor control variables, (ii) a test experiment with hydrometeor control variables but without cycling the resulting forecasts, and (iii) an experiment with hydrometeor variables and cycled forecasts. Compared with the control experiment, both hydrometeor experiments show a positive impact up to 9 hr of forecasts in terms of cloud cover, and in the first hour for precipitation. The spin‐up period, as evidenced by a reduction of precipitation forecast skill at the beginning of the forecasts, is reduced in length and intensity in the hydrometeor experiments.

Evaluation of two observation operator schemes for wind profiler radar data assimilation and its impacts on short-term forecasting

The assimilation of wind profiler (profiler) observations has the potential of improving the model initial condition of dynamical field with its high spatial and temporal wind field information. In this study, the impacts of two kinds of wind observation operators, i.e., eastward and northward velocities (u and v; uv_scheme) and wind speed and direction (spd and dir; sd_scheme), on analyses and forecasts are discussed to determine the optimal observation operator for profiler data assimilation. To compare the performance of the two observation operators on profiler data assimilation, we carry out a series of single wind tests and a two-week period cycling assimilation and forecasting experiments. The single wind tests show that the adjustment of the two observation operators to the analyses of wind can be significantly different with large innovation (OMB) of dir. The uv_scheme adjusts the corresponding model variables by ingesting profiler information of u and v winds, so that the analyzed spd is indirectly affected by the observed dir. By contrast, the changes in observed dir will not affect the analyzed spd in sd_scheme, as the observed spd and dir are independently assimilated. The case diagnoses illustrate that the sd_scheme generates the best analysis of wind in RMSE when validating against either the profiler or the pilot observations. The improved analysis of winds in turn leads to better wind forecasts and precipitation prediction, confirming the advantage of sd_scheme. Results from the cycling experiments further demonstrate that, compared with the uv_scheme, the sd_scheme shows decent improvements in wind forecasts, thus leading to better precipitation forecast skills.