Ensemble Forecast System Research Articles

Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models

Abstract. Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.

Enhancing Subseasonal Temperature Prediction by Bridging a Statistical Model With Dynamical Arctic Oscillation Forecasting

AbstractThis study proposes a hybrid approach to improving subseasonal prediction skills by bridging a conventional statistical model and a dynamical ensemble forecast system. Based on the perfect prognosis method, the phase of the Arctic Oscillation (AO) from the European Centre for Medium‐range Weather Forecasts ensemble forecast system is used as a predictor in a composite based statistical model to predict the wintertime surface air temperature in the Northern Hemisphere. The hybrid model, which employs AO phases predicted by the dynamical model for weeks 1–4, generally outperforms the conventional statistical model for lead times of weeks 2–6. The improved skill score is due to the high accuracy of the AO forecast from the dynamical model and the strong lagged connection between the AO and temperature. This study thus lays the groundwork for the potential use of combining climate variability, statistical relation, and dynamical forecasting.

Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection

Wind energy is becoming increasingly competitive and promising for renewable energy profiles. Accurate and reliable wind speed prediction is crucial for the effective exploitation of wind energy. However, previous studies have generally ignored the importance of capturing both linear and non-linear wind speed characteristics and selecting forecasting sub-models objectively, resulting in poor forecasting performance. To bridge these gaps, a novel ensemble forecasting system is proposed by integrating the decomposition strategy, sub-model selection, and ensemble point and interval prediction based on the newly proposed multi-objective Archimedes optimization algorithm, which has been demonstrated to be effective at the theoretical and empirical levels for providing reliable wind speed forecasting results. Based on hourly-resolution wind speed data from three datasets of Shandong Peninsula, China, three experiments and discussions are conducted. Simulation results demonstrate that the proposed system is capable of obtaining a high degree of precision and reliability for both point and interval forecasting relative to other comparative models. Thus, it can provide credible references for power system dispatching and management.

Estimating precipitable water vapour using the global positioning system and radio occultation over Ethiopian regions

ABSTRACT The Global Positioning System (GPS) is a space-based radio positioning system, which is capable of providing continuous position, velocity, and time information to users anywhere on, or near, the surface of the Earth. The main objective of this work was to estimate the integrated precipitable water vapour (IPWV) using ground GPS and Low Earth Orbit (LEO) Radio Occultation (RO) to study spatial-temporal variability. For LEO-GPS RO, we used Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) datasets. We estimated the daily and monthly mean of IPWV using six selected ground-based GPS stations for the years 2012 to 2016. The main perspective for selecting the years 2012 to 2016 is that, continuous data were available during these periods at all Ethiopian GPS stations. We studied temporal, seasonal, diurnal, and vertical variations of precipitable water vapour using GPS observables extracted from the precise geodetic GAMIT-GLOBK software package. Finally, we determined the cross-correlation of our GPS-derived IPWV values with those of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 Interim reanalysis and of the second generation National Oceanic and Atmospheric Administration (NOAA) model ensemble Forecast System Reforecast (GEFS/R) for validation and static comparison. There are higher values of the IPWV range from 30 to 37.5 millimetres (mm) in Gambela and Southern Regions of Ethiopia. Some parts of Tigray, Amhara, and Oromia regions had low IPWV ranges from 8.62 to 15.27 mm. The correlation coefficient between GPS-derived IPWV with ECMWF and GEFS/R exceeds 90%. We conclude that there are highly temporal, seasonal, diurnal, and vertical variations of precipitable water vapour in the study area.

Medium-range forecasting of daily reference evapotranspiration across China using numerical weather prediction outputs downscaled by extreme gradient boosting

Ahead forecasting of daily reference evapotranspiration (ET0) is of great significance for real-time irrigation decision-making, water resources allocation and hydrological drought assessment. This study for the first time evaluated the forecasting performances of 16 d ahead daily weather variables and subsequent ET0 at 51 weather stations across various climatic zones of China using the Global Ensemble Reforecast v2 Data provided by the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) model. Particularly, a novel machine learning model, i.e., extreme gradient boosting (XGBoost), was proposed to downscale and bias correct the GEFS forecasts. The results showed that the GEFS forecasts downscaled by the XGBoost model were more accurate than those downscaled by the inverse distance weighting (IDW) and equidistant cumulative distribution functions matching (EDCDFm) methods, with decreases in mean RMSE of maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), wind speed (Ws) and solar radiation (Rs) by 36.2%, 33.5%, 35.7%, 75.0% and 27.0%, and by 11.2%, 13.3%, 14.0%, 26.4% and 13.2%, resulting in decreases in mean RMSE of ET0 by 35.0% and 18.2%, respectively. The overall forecasting performance of Tmin (NRMSE = 0.14–0.27, R2 = 0.82–0.95) was best, followed by Tmax (NRMSE = 0.11–0.26, R2 = 0.74–0.94), Rs (NRMSE = 0.23–0.41, R2 = 0.34–0.77), RH (NRMSE = 0.18–0.26, R2 = 0.27–0.72) and finally Ws (NRMSE = 0.34–0.46, R2 = 0.22–0.58), and their forecasting performances decreased with the increase in lead time. Nationally averaged RMSE values of daily ET0 for 16 d lead time (1.0 mm d−1) and 8 d lead time (0.9 mm d−1) were approximately 1.7 and 1.5 times of that for 1 d lead time (0.6 mm d−1), respectively. Average R2 values of ET0 across China varied 0.56–0.85 for 1–16 d lead times, while the corresponding NRMSE values ranged 0.22–0.38, with more accurate ET0 estimates in the temperate continental zone (TCZ) and mountain plateau zone (MPZ) than the temperate monsoon zone (TMZ) and (sub)tropical monsoon zone (SMZ). The forecasting performance of seasonal ET0 was generally better in summer, followed by autumn, spring and winter. The greatest reduction in the forecasting performance of daily ET0 was produced by the forecasting error of Rs in all climatic zones, followed by Ws, Tmax and RH in TCZ and MPZ, and by RH, Ws and Tmax in TMZ and SMZ; however, the influence of Tmin was marginal in all climatic zones. The results indicated that daily ET0 across China can be satisfactorily forecasted using the GEFS outputs downscaled by the XGBoost model, particularly for 1–8 d lead times.

Seasonal ensemble forecasts for soil moisture, evapotranspiration and runoff across Australia

The seasonal predictability of hydrological variables at high resolution across Australia was assessed using a new national seasonal ensemble forecasting system for soil moisture, evapotranspiration and runoff. The system consists of a gridded water balance model (AWRA-L) forced with downscaled and calibrated seasonal climate forecasts from a climate modelling system, ACCESS-S1. Here, we evaluate the hydrological forecasts at one- to six-months lead time relative to a historical reference simulation forced with observed climate inputs. Deterministic and probabilistic skill, accuracy and reliability of the forecast ensemble were assessed, with a specific focus on forecasts of hydrological extremes. Additionally, we assess the performance of the hindcast for selected use cases, particularly focusing on agriculture and water management.Nationally, our assessment of the hydrological forecasts indicated good skill for all variables at one-month lead time, and up to three-months lead time for soil moisture and actual evapotranspiration in many key regions in Australia. Regarding hydrological extremes, we find particularly high skill at longer lead times for low soil moisture and actual evapotranspiration events, highlighting the potential value for applications such as hydrological drought prediction or bushfire risk assessments. Bias of the hydrological forecasts based on calibrated seasonal climate forecasts was shown to be close to zero for all variables, regions and lead times and insignificant, with a minor exception for potential evapotranspiration.More detailed evaluation across economically important regions and river basins, in particular, the Australian Wheatbelt and the Murray-Darling Basin (the largest river basin in Australia and an economically and environmentally critical region), highlights promising potential for the soil moisture and evapotranspiration forecast to be used in applications in agriculture, such as for optimising irrigation or scheduling sowing and harvesting dates. For hydrologically important headwater regions across the Great Dividing Range and the alpine regions of Victoria, the forecasts of runoff show good skill at longer lead times indicating the value of the forecasts for reservoir management.Overall, we conclude that the forecasting system shows sufficient skill for a wide range of applications and regions up to a lead time of three months. We outline limitations of the presented system and highlight potential future research directions.

Understanding Sea Level Processes During Western Mediterranean Storm Gloria

In January 2020, the storm Gloria hit the Western Mediterranean Sea causing severe coastal damages, destruction of infrastructures, flooding and several casualties. This extreme event was characterized by strong Eastern winds, record-breaking waves heights and periods, and a storm surge that locally beat the record along Valencia’s coastline. This paper analyses the dynamic evolution of sea level during this storm. The study employs both the in situ data and the operational forecasts of the PORTUS early warning system. Tide gauge data are analyzed on the different temporal scales that contribute to total sea level: long-term and seasonal, tides and storm surges, and higher frequency oscillations. It was found that, due to the unusual long wave periods, infragravity waves were generated and dominate the high frequency energy band, contributing significantly to extreme sea level records. This is a relevant finding, since this kind of oscillations are usually associated with larger basins, where swell can develop and propagate. The impact of sea level rise is also analyzed and considered relevant. A multi-model ensemble storm surge forecasting system is employed to study the event. The system was able to correctly forecast the surge, and the measured data were always inside the confidence bands of the system. The differences of the results obtained by the available operational forecasting system integrated into the ensemble, including those from Copernicus Marine Service, are described. All the models provided useful forecasts during the event, but differences with measured data are described and connected with the known limitations in physics (for example, barotropic vs. baroclinic) and set-up of the models (model domain, lack of tides and different inverse barometer implementations at the open boundaries amongst others).

A Multivariate Approach to Generate Synthetic Short‐To‐Medium Range Hydro‐Meteorological Forecasts Across Locations, Variables, and Lead Times

AbstractThe use of hydro‐meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance. Methods for generating synthetic forecasts of hydro‐meteorological variables are crucial for robust validation of forecast use, as numerical weather prediction hindcasts are only available for a relatively short period (10–40 years) that is insufficient for assessing risk related to forecast‐informed decision‐making during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto‐correlated, and non‐Gaussian behavior. An empirical copula is used to capture covariance between variables, forecast lead times, and across space. We demonstrate the method for medium‐range forecasts across Northern California in two case studies for (1) streamflow and (2) temperature and precipitation, which are based on hindcasts from the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS) and the NCEP GEFS/R V2 climate model, respectively. The case studies highlight the flexibility of the model and its ability to emulate space‐time structures in forecasts at scales critical for water resources management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for risk analysis.

A Progress Report on the Development of the High-Resolution Rapid Refresh Ensemble

AbstractThe High-Resolution Rapid Refresh Ensemble (HRRRE) is a 36-member ensemble analysis system with 9 forecast members that utilizes the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) dynamic core and the physics suite from the operational Rapid Refresh/High-Resolution Rapid Refresh deterministic modeling system. A goal of HRRRE development is a system with sufficient spread among members, comparable in magnitude to the random error in the ensemble mean, to represent the range of possible future atmospheric states. HRRRE member diversity has traditionally been obtained by perturbing the initial and lateral boundary conditions of each member, but recent development has focused on implementing stochastic approaches in HRRRE to generate additional spread. These techniques were tested in retrospective experiments and in the May 2019 Hazardous Weather Testbed Spring Experiment (HWT-SE). Results show a 6%–25% increase in the ensemble spread in 2-m temperature, 2-m mixing ratio, and 10-m wind speed when stochastic parameter perturbations are used in HRRRE (HRRRE-SPP). Case studies from HWT-SE demonstrate that HRRRE-SPP performed similar to or better than the operational High-Resolution Ensemble Forecast system, version 2 (HREFv2), and the nonstochastic HRRRE. However, subjective evaluations provided by HWT-SE forecasters indicated that overall, HRRRE-SPP predicted lower probabilities of severe weather (using updraft helicity as a proxy) compared to HREFv2. A statistical analysis of the performance of HRRRE-SPP and HREFv2 from the 2019 summer convective season supports this claim, but also demonstrates that the two systems have similar reliability for prediction of severe weather using updraft helicity.

Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling

AbstractAccurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events. The skill of the multi‐model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0–5 day model accumulation than for the first 24 h, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias correction. Compared to individual models, the multi‐model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 h lead times.

FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction

The reduction of visibility adversely affects land, marine, and air transportation. Thus, the ability to skillfully predict fog would provide utility. We predict fog visibility categories below 1600 m, 3200 m and 6400 m by post-processing numerical weather prediction model output and satellite-based sea surface temperature (SST) using a 3D-Convolutional Neural Network (3D-CNN). The target is an airport located on a barrier island adjacent to a major US port; measured visibility from this airport serves as a proxy for fog that develops over the port. The features chosen to calibrate and test the model originate from the North American Mesoscale Forecast System, with values of each feature organized on a 32 × 32 horizontal grid; the SSTs were obtained from the NASA Multiscale Ultra Resolution dataset. The input to the model is organized as a high dimensional cube containing 288 to 384 layers of 2D horizontal fields of meteorological variables (predictor maps). In this 3D-CNN (hereafter, FogNet), two parallel branches of feature extraction have been designed, one for spatially auto-correlated features (spatial-wise dense block and attention module), and the other for correlation between input variables (variable-wise dense block and attention mechanism.) To extract features representing processes occurring at different scales, a 3D multiscale dilated convolution is used. Data from 2009 to 2017 (2018 to 2020) are used to calibrate (test) the model. FogNet performance results for 6, 12− and 24−h lead times are compared to results from the High-Resolution Ensemble Forecast (HREF) system. FogNet outperformed HREF using 8 standard evaluation metrics.

Improving short to medium range GEFS precipitation forecast in India

This study aims to enhance daily precipitation forecasts over the Indian subcontinent through post-processing the Global Ensemble Forecast System (GEFS) outputs using Analog (AN) and Logistic Regression (LR) techniques. Both raw and post-processed GEFS precipitation forecasts were evaluated against the Indian Meteorological Department observed dataset using probabilistic and deterministic forecast evaluation metrics, namely Brier Skill Score (BSS) and Root Mean Square Error (RMSE), respectively. Results found that the LR and AN post-processing method considerably improved short to medium range (1–7 day) precipitation forecasts over India. A comparison of the techniques with GEFS version 12 (GEFSv12) forecasts across different basins suggests that both methods were able to provide skillful precipitation forecasts in all the river basins, except that the AN method underperformed for the western ghats. The seasonal analysis also showed that the raw and the post-processed forecasts under-performed during the monsoon season while performing comparatively well during the non-monsoon season. The comparison of LR and AN methods showed that LR outperformed the AN method. The forecasts enhanced using both the post-processing techniques were more reliable and skillful than the GEFSv12 precipitation forecast. Comparison of the recent GEFS version with the older version of GEFS showed that the performance of the earlier version was slightly better compared to the latest version, however none of the forecasts can be used for decision making purposes without post-processing.

Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm

Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the irregularity of wind speed makes it difficult to integrate wind energy into smart grids. Thus, achieving credible and effective wind speed forecasting results is crucial for the operation and management of wind energy. In this study, we propose an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data. After decomposition using the data decomposition technology, each decomposed wind speed series is considered as the input to multiple predictors, from which the optimal forecasting model for each sub-series is determined based on sub-model selection. To obtain reliable forecasting results, a novel multi-objective version of the Mayfly algorithm is proposed to estimate the optimal weight coefficients for integrating the forecasting values of the sub-series. Based on three experiments and four analyses, the proposed ensemble system is verified as effective for obtaining accurate and stable point forecasting and interval forecasting performances, thus aiding in the planning and dispatching of power grids.

A Summary of GFS Ensemble Integrated Water Vapor Transport Forecasts and Skill along the U.S. West Coast during Water Years 2017–20

AbstractThe ability to provide accurate forecasts and improve situational awareness of atmospheric rivers (ARs) is key to impact-based decision support services and applications such as forecast-informed reservoir operations. The purpose of this study is to quantify the cool-season water year skill for 2017–20 of the NCEP Global Ensemble Forecast System forecasts of integrated water vapor transport along the U.S. West Coast commonly observed during landfalling ARs. This skill is summarized for ensemble probability-over-threshold forecasts of integrated water vapor transport magnitudes ≥ 250 kg m−1 s−1 (referred to as P250). The P250 forecasts near North-Coastal California at 38°N, 123°W were reliable and successful at lead times of ~8–9 days with an average success ratio > 0.5 for P250 forecasts ≥ 50% at lead times of 8 days and Brier skill scores > 0.1 at a lead time of 8–9 days. Skill and accuracy also varied as a function of latitude and event characteristics. The highest (lowest) success ratios and probability of detection values for P250 forecasts ≥ 50% occurred on average across Northern California and Oregon (Southern California), whereas the average probability of detection of more intense and longer duration landfalling ARs was 0.1–0.2 higher than weaker and shorter duration events at lead times of 3–9 days. The potential for these forecasts to enhance situational awareness may also be improved, depending on individual applications, by allowing for flexibility in the location and time of verification; the success ratios increased 10%–30% at lead times of 5–10 days allowing for flexibility of ±1.0° latitude and ±6 h in verification.

A novel hybrid artificial neural network - Parametric scheme for postprocessing medium-range precipitation forecasts

Many present-day statistical schemes for postprocessing weather forecasts, in particular precipitation forecasts, rely on calibration using prescribed statistical models to relate forecast statistics to distributional parameters. The efficacy of such schemes is often constrained not only by prescribed predictor-predictand relation, but also by arbitrary choices of temporal window and lead time range for training. To address this limitation, we propose an end-to-end, computationally efficient hybrid postprocessing scheme capable of producing full predictive distributions of precipitation accumulation without explicit stratification of forecast-observation pairs by forecast lead time and season. The proposed framework uses the censored, shifted gamma distribution (CSGD) as the predictive distribution but uses an artificial neural network (ANN) to estimate the distributional parameters of CSGD through a unified approach. This approach, referred to as ANN-CSGD, allows for simultaneous estimation of distributional parameters over multiple lead times and seasons in a single model by incorporating the latter variables as predictors to the ANN. We test our proposed ANN-CSGD model for postprocessing of ensemble mean forecasts of 24-h precipitation totals over selected river basins in California, at one- to seven-day lead times, from the Global Ensemble Forecast System (GEFS). The probabilistic quantitative precipitation forecasts (PQPFs) from the ANN-CSGD, are more skillful overall than those from the benchmark CSGD and the Mixed-type meta-Gaussian distribution (MMGD) models. The ANN-CSGD PQPFs highly improve the performance of those from CSGD in predicting the probability of precipitation (PoP) and are also much sharper and reliable at higher precipitation thresholds. We demonstrate how the hybrid approach, by using the entire available training data and its modified formulation, efficiently represents interactions between GEFS forecasts and season/lead times, thus leading to enhanced predictive performance.

Impact of Land Surface Processes on a Record‐Breaking Rainfall Event on May 06–07, 2017, in Guangzhou, China

AbstractA record‐breaking extreme rainfall event with a maximum rainfall amount over 24 h of 524.1 mm occurred in Guangzhou, China, on May 06–07, 2017. To study the impact of land surface processes on this extreme rainfall, two 21‐member convective‐permitting ensemble forecasts over South China were performed based on two land surface models (LSMs), Noah and Community Land Model (CLM), and 21 forecasting members of the Global Ensemble Forecast System (GEFS). The results showed that, in general, members using the Noah LSM could better simulate urban heat islands (UHIs) and urban convection than members using the CLM LSM in this case. By investigating the ensemble member that most resembled the observations, it was found that the high temperature center in the urban area caused a thermal low in the early stage. As the southerly winds strengthened, the low‐level convergence line continued moving northward and eventually triggered convection in the mountainous region. A sensitivity experiment showed that the impact of land surface heterogeneity on precipitation could be reflected on a finer scale, and heavy rainfall was very sensitive to the changes in small‐scale land surface forces, including terrain and land use. Slight variations in small‐scale land surface conditions caused great responses in the total precipitation, indicating that for the occurrence of such quasi‐stationary extreme rainfall, a subtle balance between different atmospheric and land surface factors may be required.

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.

The Influence of Boundary Layer Mixing Strength on the Evolution of a Baroclinic Cyclone

AbstractSubgrid-scale turbulence in numerical weather prediction models is typically handled by a PBL parameterization. These schemes attempt to represent turbulent mixing processes occurring below the resolvable scale of the model grid in the vertical direction, and they act upon temperature, moisture, and momentum within the boundary layer. This study varies the PBL mixing strength within 4-km WRF simulations of a 26–29 January 2015 snowstorm to assess the sensitivity of baroclinic cyclones to eddy diffusivity intensity. The bulk critical Richardson number for unstable regimes is varied between 0.0 and 0.25 within the YSU PBL scheme as a way of directly altering the depth and magnitude of subgrid-scale turbulent mixing. Results suggest that varying the bulk critical Richardson number is similar to selecting a different PBL parameterization. Differences in boundary layer moisture availability, arising from reduced entrainment of dry, free tropospheric air, lead to variations in the magnitude of latent heat release above the warm frontal region, producing stronger upper-tropospheric downstream ridging in simulations with less PBL mixing. The more amplified flow pattern impedes the northeastward propagation of the surface cyclone and results in a westward shift of precipitation. In addition, trajectory analysis indicates that ascending parcels in the less-mixing simulations condense more water vapor and terminate at a higher potential temperature level than do ascending parcels in the more-mixing simulations, suggesting stronger latent heat release when PBL mixing is reduced. These results suggest that spread within ensemble forecast systems may be improved by perturbing PBL mixing parameters that are not well constrained.

Ensemble Methods for Neural Network‐Based Weather Forecasts

AbstractEnsemble weather forecasts enable a measure of uncertainty to be attached to each forecast, by computing the ensemble's spread. However, generating an ensemble with a good spread‐error relationship is far from trivial, and a wide range of approaches to achieve this have been explored—chiefly in the context of numerical weather prediction models. Here, we aim to transform a deterministic neural network weather forecasting system into an ensemble forecasting system. We test four methods to generate the ensemble: random initial perturbations, retraining of the neural network, use of random dropout in the network, and the creation of initial perturbations with singular vector decomposition. The latter method is widely used in numerical weather prediction models, but is yet to be tested on neural networks. The ensemble mean forecasts obtained from these four approaches all beat the unperturbed neural network forecasts, with the retraining method yielding the highest improvement. However, the skill of the neural network forecasts is systematically lower than that of state‐of‐the‐art numerical weather prediction models.

A study of predictability of coupled ocean\u2013atmosphere system using attractor radius and global attractor radius

While ocean–atmosphere coupled models play an increasingly important role in weather-climate simulation and prediction, the predictability theory based on an atmosphere-only model has significant limitations in interpreting prediction results and guiding predictability studies. Here we use a conceptual ocean–atmosphere coupled model that describes the typical interactions of a synoptic-scale atmosphere with a seasonally-interannually varying upper ocean as well as a deep ocean that varies on decadal timescales to systematically study the predictability of a coupled system. Moving from an atmosphere-only system to an ocean–atmosphere coupled system, the initial-value predictability problem becomes a joint initial-value and boundary-value problem. Although the coupling process increases the uncertainties of the boundary, ocean signals with longer timescales are added to the atmosphere system, thus increasing its predictability. We then investigate the predictability characteristics of the National Centers for Environmental Prediction coupled Climate Forecast System (CFS) and the uncoupled Global Ensemble Forecast System (GEFS). In the coupled CFS system, the practical predictability limit of the lower troposphere is significantly longer than in the uncoupled GEFS due to the contribution of low-frequency boundary signals from air-sea interactions. While further deep and thorough examination is necessary for understanding ocean predictability in the climate system, a preliminary discussion for the predictability of the upper and deep oceans within a coupled ocean–atmosphere framework is also presented in this study.