Short-range Ensemble Forecasts Research Articles

Enhancing irrigation water productivity using short-range ensemble weather forecasts at basin scale: A novel framework for regions with high hydro-climatic variability

Integrating short-term weather forecasts with crop growth models has emerged as a valuable decision-support tool for enhancing irrigation water productivity in water-intensive crops. Our primary focus lies in formulating a simplified Quasi-Farmer Behavior Routine to utilize ensemble weather forecasts to provide irrigation guidance. The study proposes the concept of Rainfall Confidence Quotient (RCQ) that is optimized based on the forecast uncertainty levels for rainfall categories that affect the irrigation decision. We utilize two publicly available ensemble weather forecasts from NCEP-GEFS and ECM and a regionally fine-tuned high-resolution Weather Research and Forecasting (WRF) ensemble forecast system with a 3-day lead time. We have refined our methodology by conducting 800 simulation experiments across diverse scenarios, each reflecting distinct climate regimes, soil types, cropping seasons, and management practices. The proposed method consists of a sequential 3-checkpoint algorithm incorporating ensemble rainfall forecasts, antecedent soil moisture, and evapotranspiration. During the wet crop season of 2015–16, the enhanced methodology yielded a substantial increase in irrigation water productivity (IWP), averaging around 20–30 % increase across spatial locations, in contrast to the conventional irrigation practices that do not consider weather forecasts. However, during 2016–17, a dry year, the improvement in IWP only ranged between 2–10 %. On average, water savings (at field scale) of 200–500 mm were recorded in the wet year crop season of 2015–16, while 100–300 mm of water savings were achieved during the dry year crop season of 2016–17, without any considerable reductions in the crop yield. The robustness and adaptability of the developed approach have been established through comprehensive evaluation with field observation and remote sensing techniques, suggesting its potential scalability to similar hydro-climatic typologies across the world.

Assessing the ability of a new seamless short-range ensemble rainfall product to anticipate flash floods in the French Mediterranean area

Abstract. Flash floods have dramatic economic and social consequences, and efficient adaptation policies are required to reduce their impacts, especially in the context of global change. Developing more efficient flash flood forecasting systems can largely contribute to these adaptation requirements. The aim of this study was to assess the ability of a new seamless short-range ensemble quantitative precipitation forecast (QPF) product, called PIAF-EPS (Prévision Immédiate Agrégée Fusionnée ensemble prediction system) and recently developed by Météo-France, to predict flash floods when used as input to an operational hydrological forecasting chain. For this purpose, eight flash flood events that occurred in the French Mediterranean region between 2019 and 2021 were reanalysed, using a hydrological-modelling chain similar to the one implemented in the French Vigicrues Flash operational flash flood monitoring system. The hydrological forecasts obtained from PIAF-EPS were compared to the forecasts obtained with different deterministic QPFs from which PIAF-EPS is directly derived. The verification method applied in this work uses scores calculated on contingency tables and combines the forecasts issued on each 1 km2 pixel of the territory. This offers a detailed view of the forecast performances, covering the whole river network and including the small ungauged rivers. The results confirm the added value of the ensemble PIAF-EPS approach for flash flood forecasting, in comparison to the different deterministic scenarios considered.

Evaluation of Probabilistic Snow Forecasts for Winter Weather Operations at Intermountain West Airports

Abstract This work set out to assess the performance of four forecast systems [the Short-Range Ensemble Forecast (SREF), High-Resolution Rapid Refresh Ensemble (HRRRE), the National Blend of Models (NBM), and the Probabilistic Snow Accumulation product (PSA) from the National Weather Service (NWS) Boulder, Colorado, Weather Forecast Office] when predicting snowfall events around the Intermountain West to advise winter weather decision-making processes at Denver International Airport. The goal was to provide airport personnel and the Boulder NWS Forecast Office with operationally relevant verification results on the timing and severity of these events so they are able to make better-informed decisions to minimize negative impacts of storms. Forecasts of snow events using various probability thresholds and a climatological snow-to-liquid ratio of 15:1 were evaluated against Meteorological Aerodrome Reports (METARs) for 24-h periods following four decision-making times spaced equally throughout the day. For the ensembles, a frequentist approach was used: the forecast probability equaled the percentage of ensemble members that predicted a snow event. The results show that the NBM had the best timing of snow events out of the products, while all the products tended to overforecast snow amount. Additionally, NBM had fewer snow events and rarely had high probabilities of snow, unlike the other forecast products.

The Lateral Boundary Perturbations Growth and Their Dependence on the Forcing Types of Severe Convection in Convection-Allowing Ensemble Forecasts

The application of lateral boundary perturbations (LBPs) helps to restore dispersion in convection-allowing ensemble forecasts (CAEFs). However, the applicability of LBPs remains unclear because of the differences between convection systems. Short-range (24 h) ensemble forecasts are carried out to explore this issue with a strong-forcing (SF) case and a weak-forcing (WF) case in East China. The dependence of LBPs on the forcing types of severe convection is investigated regarding the forecast error growth caused by the lateral boundary conditions (LBCs). The results show that the LBPs mainly influence the SF case rather than the WF case, especially after a 12-h forecast. The large-scale errors dominate in the SF case because the change in the synoptic-scale system affects the forecast error evolution. In contrast, the large-scale errors are mainly derived from the upscaling of the small-scale errors in the WF case, indicating that using LBPs is only insufficient in such a case. In sensitivity experiments that vary the magnitude of LBPs from 10% to 150% of its original value, CAEFs demonstrate more sensitive to LBPs in the SF case than in the WF case, indicating that the WF case has intrinsically limited predictability. Overall, LBPs are more suitable for the SF case, while additional perturbations from other sources are required for CAEFs in the WF case because of the limits of intrinsic predictability.

Utilizing the High-Resolution Ensemble Forecast System to Produce Calibrated Probabilistic Thunderstorm Guidance

Abstract The High-Resolution Ensemble Forecast system (HREF) calibrated thunder guidance is a suite of probabilistic forecast products designed to predict the likelihood of at least one cloud-to-ground (CG) lightning flash within 20 km (12 miles) of a point during a given 1-, 4-, and 24-h time interval. This guidance takes advantage of a combination of storm attribute and environmental fields produced by the convection-allowing HREF to objectively improve upon lightning forecasts generated by the non-convection-allowing Short-Range Ensemble Forecast system (SREF). This study provides an overview of how the HREF calibrated thunder guidance was developed and calibrated to be statistically reliable against observed CG lightning flashes recorded by the National Lightning Detection Network (NLDN). Performance metrics for the 1-, 4-, and 24-h guidance are provided and compared to the respective SREF calibrated probabilistic lightning forecasts. The HREF calibrated thunder guidance has been implemented operationally within the National Weather Service and is now available to the public. Significance Statement The NOAA Storm Prediction Center has created a suite of new calibrated probabilistic thunderstorm guidance products from a convection-allowing model ensemble, the HREF. The new guidance is a notable improvement over the long-running SREF calibrated thunder guidance and is now operational across the National Weather Service.

Ground-level ozone simulation using ensemble WRF/Chem predictions over the Southeast United States

Being detrimental to human health and vegetation growth, ground-level ozone (O3) is becoming a huge concern as an air pollutant. The processes of formation, diffusion, transformation, and transport of O3 in the atmosphere are highly affected by meteorological conditions such as solar radiation, temperature, precipitation, and wind. Chemical transport models (CTMs) are widely used in simulating O3 pollution with two main inputs of the meteorological condition and emission inventory. Meteorological inputs play a crucial role in the model simulation accuracy especially in areas where emission has been well constrained such as the United States (U.S.). However, most O3 simulations today still use only one set of meteorological input, which leaves room for model performance improvement by using ensemble meteorological conditions. In this study, O3 over the Southeast U.S. was simulated for one week in the summer of each year from 2016 to 2018 by using ensemble meteorological inputs offered by Short Range Ensemble Forecast products. The predictions were conducted through the Weather Research and Forecasting model coupled with Chemistry. The calculated ensemble prediction results got at least 66.7% improvement in agreement with O3 observations compared with single runs in the three selected cities (Miami, Atlanta, and Baton Rouge) from 2016 to 2018. This study emphasized the accuracy and provided a new idea of using ensemble meteorological inputs to improve O3 prediction than using traditional single meteorology by CTMs.

Comparison of different land-surface perturbation methods in short-range ensemble forecasts

In order to compare the sensitivity of short-range ensemble forecasts to different land-surface parameters in the South China region, three perturbation experiments related to the land surface model (LSM), initial soil moisture (ISM), and land–atmosphere coupling coefficient (LCC) were designed, and another control experiment driven by the Global Ensemble Forecast System (GEFS) was also performed. All ensemble members were initiated at 0000 UTC each day, and integrated for 24 h for a total of 40 days from the period 1 April to 10 May 2019 based on the Weather Research and Forecasting model. The results showed that the perturbation experiment of the LSM (LSMPE) had the largest ensemble spread, as well as the lowest ensemble-mean root-mean-square error among the three sets of land-surface perturbed experiments, which indicated that it could represent more uncertainty and less error. The ensemble spread of the perturbation experiment of the ISM (ISMPE) was generally less than that of LSMPE but greater than that of LCCPE (the perturbation experiment of the LCC). In particular, although the perturbation of the LCC could not produce greater spread, it had an effective influence on the intensity of precipitation. However, the ensemble spread of all the land-surface perturbed experiments was smaller than that of GEFSPE (the control experiment). Therefore, in future, land-surface perturbations and atmospheric perturbations should be combined in the design of ensemble forecasting systems to make the model represent more uncertainties.摘要为了比较不同陆面扰动方法对短时集合预报的影响, 本研究设计了陆面模式扰动实验 (LSMPE), 初始土壤湿度扰动实验 (ISMPE), 陆面-大气耦合系数扰动实验 (LCCPE) 以及大气扰动对照实验 (GEFSPE). 结果表明, 在三组陆面扰动实验中, LSMPE能代表最大的不确定性且误差最小;ISMPE的离散度要比LCCPE稍大, 但是耦合系数的减弱能有效降低区域平均降水量.然而, 所有陆面扰动实验产生的离散度均小于GEFSPE, 这表明未来有必要将陆面扰动与大气扰动相结合, 以使得模式能代表更多的不确定性.

Impact of AERI Temperature and Moisture Retrievals on the Simulation of a Central Plains Severe Convective Weather Event

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors.

Parameter Sensitivity of the WRF–LETKF System for Assimilation of Radar Observations: Imperfect-Model Observing System Simulation Experiments

AbstractSpecification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (~3.6–7.3 km) and large RTPS inflation parameter (~0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.

Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

AbstractFive sets of 48-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were systematically evaluated over the conterminous United States with a focus on precipitation across 31 cases. The various CAEs solely differed by their initial condition perturbations (ICPs) and central initial states. CAEs initially centered about deterministic Global Forecast System (GFS) analyses were unequivocally better than those initially centered about ensemble mean analyses produced by a limited-area single-physics, single-dynamics 15-km continuously cycling ensemble Kalman filter (EnKF), strongly suggesting relative superiority of the GFS analyses. Additionally, CAEs with flow-dependent ICPs derived from either the EnKF or multimodel 3-h forecasts from the Short-Range Ensemble Forecast (SREF) system had higher fractions skill scores than CAEs with randomly generated mesoscale ICPs. Conversely, due to insufficient spread, CAEs with EnKF ICPs had worse reliability, discrimination, and dispersion than those with random and SREF ICPs. However, members in the CAE with SREF ICPs undesirably clustered by dynamic core represented in the ICPs, and CAEs with random ICPs had poor spinup characteristics. Collectively, these results indicate that continuously cycled EnKF mean analyses were suboptimal for CAE initialization purposes and suggest that further work to improve limited-area continuously cycling EnKFs over large regional domains is warranted. Additionally, the deleterious aspects of using both multimodel and random ICPs suggest efforts toward improving spread in CAEs with single-physics, single-dynamics, flow-dependent ICPs should continue.

Bias Correction of Short-Range Ensemble Forecasts of Daily Maximum Temperature Using Decaying Average

In this study, we assessed the performance of the decaying average bias correction method in removing the systematic error in daily maximum temperature (dTmax) ensemble forecasts. We applied the technique to a short-range high-resolution limited-area ensemble prediction system of the Korea Meteorological Administration, which shows under-predictive and under-dispersive characteristics for dTmax. The bias correction was applied to the grid of the model using spatial interpolation of the decaying average bias from surrounding reference points. The method was verified by evaluating the accuracy of the ensemble mean, spread-skill relationship, and the performance of the probabilistic forecasts. The results showed that the decaying average technique minimized the systematic error in the ensemble mean and improved the performance of the probabilistic forecasts. The overall mean absolute error of the ensemble mean was lowered from 2.2 to 1.2 ∘C and the root-mean-square error from 2.5 to 1.6 ∘C. The continuous ranked probability score decreased from 1.9 to 1.0 ∘C. The reliability of three dichotomous events also improved and the Brier skill scores increased. However, the bias correction only slightly affected the ensemble spread, and the system remained under-dispersive.

Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests

Abstract Most ensembles suffer from underdispersion and systematic biases. One way to correct for these shortcomings is via machine learning (ML), which is advantageous due to its ability to identify and correct nonlinear biases. This study uses a single random forest (RF) to calibrate next-day (i.e., 12–36-h lead time) probabilistic precipitation forecasts over the contiguous United States (CONUS) from the Short-Range Ensemble Forecast System (SREF) with 16-km grid spacing and the High-Resolution Ensemble Forecast version 2 (HREFv2) with 3-km grid spacing. Random forest forecast probabilities (RFFPs) from each ensemble are compared against raw ensemble probabilities over 496 days from April 2017 to November 2018 using 16-fold cross validation. RFFPs are also compared against spatially smoothed ensemble probabilities since the raw SREF and HREFv2 probabilities are overconfident and undersample the true forecast probability density function. Probabilistic precipitation forecasts are evaluated at four precipitation thresholds ranging from 0.1 to 3 in. In general, RFFPs are found to have better forecast reliability and resolution, fewer spatial biases, and significantly greater Brier skill scores and areas under the relative operating characteristic curve compared to corresponding raw and spatially smoothed ensemble probabilities. The RFFPs perform best at the lower thresholds, which have a greater observed climatological frequency. Additionally, the RF-based postprocessing technique benefits the SREF more than the HREFv2, likely because the raw SREF forecasts contain more systematic biases than those from the raw HREFv2. It is concluded that the RFFPs provide a convenient, skillful summary of calibrated ensemble output and are computationally feasible to implement in real time. Advantages and disadvantages of ML-based postprocessing techniques are discussed.

Effect of Meteorological Variability on Fine Particulate Matter Simulations Over the Contiguous United States

AbstractThis study quantifies the impact of meteorological variability on the Community Multiscale Air Quality (CMAQ) model‐simulated particulate matter of aerodynamic diameter 2.5 μm or smaller (particulate matter 2.5 [PM2.5]) over the contiguous United States (CONUS). The meteorological variability is represented using the Short‐Range Ensemble Forecast (SREF) produced operationally by the National Oceanic and Atmospheric Administration. A hierarchical cluster analysis technique is applied to down‐select a subset of the SREF members that objectively accounts for the overall meteorological forecast variability of SREF. Three SREF members are selected to drive off‐line CMAQ simulations during January, April, July, and October 2016. Changes in emissions, vertical diffusion, and aerosol processes due to meteorological variability dominate changes in aerosol mass concentrations over 55‐73% of the domain except in July when dry deposition dominates emissions and aerosol processes. Weather Research and Forecasting‐Advanced Research WRF (WRF‐ARW) simulations reproduced the variability of surface temperature very well but overestimated the 10‐m wind speed, precipitation, and at some sites the planetary boundary layer height. Averaged over CONUS, CMAQ simulations driven by all three meteorological configurations capture the observed daytime low and nighttime high PM2.5 mass concentrations but underestimated the observed concentrations likely due to faster advection and higher wet deposition in the model. PM2.5 levels across the three simulations agreed well during daytime but showed larger variability during nighttime due to dominance of aerosol, clouds, and advection processes in nighttime. The meteorology‐induced variability in PM2.5 is estimated to be 0.08–24 μg/m3 over the CONUS with larger variability over the eastern United States.

Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

AbstractThe National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.

On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure

AbstractCloud-computing resources are increasingly used in atmospheric research and real-time weather forecasting. The aim of this study is to explore new ways to reduce cloud-computing costs for real-time numerical weather prediction (NWP). One way is to compress output files to reduce data egress costs. File compression techniques can reduce data egress costs by over 50%. Data egress costs can be further minimized by postprocessing in the cloud and then exporting the smaller resulting files while discarding the bulk of the raw NWP output. Another way to reduce costs is to use preemptible resources, which are virtual machines (VMs) on the Google Cloud Platform (GCP) that clients can use at an 80% discount (compared to nonpreemptible VMs), but which can be turned off by the GCP without warning. By leveraging the restart functionality in the Weather Research and Forecasting (WRF) Model, preemptible resources can be used to save 60%–70% in weather simulation costs without compromising output reliability. The potential cost savings are demonstrated in forecasts over the Canadian Arctic and in a case study of NWP runs for the West African monsoon (WAM) of 2017. The choice in model physics, VM specification, and use of the aforementioned cost-saving measures enable simulation costs to be low enough such that the cloud can be a viable platform for running short-range ensemble forecasts when compared to the cost of purchasing new computer hardware.

A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Furthermore, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American mesoscale forecast system, the global forecast system, and the short-range ensemble forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error has been reduced by 13.80% to 61.21% when compared with three tested baselines.

Impact of Soil Moisture Uncertainty on Summertime Short-range Ensemble Forecasts

To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts (SREFs), a fivemember SREF experiment with perturbed initial soil moisture (ISM) was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 hPa, and produced skillful ensemble-mean 24-h accumulated precipitation (APCP24) forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.

Validation of Mountain Precipitation Forecasts from the Convection-Permitting NCAR Ensemble and Operational Forecast Systems over the Western United States

Abstract Convection-permitting ensembles can capture the large spatial variability and quantify the inherent uncertainty of precipitation in areas of complex terrain; however, such systems remain largely untested over the western United States. In this study, we assess the capabilities of deterministic and probabilistic cool-season quantitative precipitation forecasts (QPFs) produced by the 10-member, convection-permitting (3-km horizontal grid spacing) NCAR Ensemble using observations collected by SNOTEL stations at mountain locations across the western United States and precipitation analyses from PRISM. We also examine the performance of operational forecast systems run by NCEP including the High Resolution Rapid Refresh (HRRR) model, the NAM forecast system with a 3-km continental United States (CONUS) nest, GFS, and the Short-Range Ensemble Forecast system (SREF). Overall, we find that higher-resolution models, such as the HRRR, NAM-3km CONUS nest, and an individual member of the NCAR Ensemble, are more deterministically skillful than coarser models, especially over the narrow interior ranges of the western United States, likely because they better resolve topography and thus better simulate orographic precipitation. The 10-member NCAR Ensemble is also more probabilistically skillful than 13-member subensembles composed of each SREF dynamical core, but less probabilistically skillful than the full 26-member SREF, as a result of insufficient spread. These results should help guide future short-range model development and inform forecasters about the capabilities and limitations of several widely used deterministic and probabilistic modeling systems over the western United States.

Evaluation and Postprocessing of Ensemble Fire Weather Predictions over the Northeast United States

AbstractThe Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

Eastern U.S. Verification of Ensemble Precipitation Forecasts

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.