Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Evaluating the Robustness of MDSS Maintenance Forecasts Using Connected Vehicle Data

The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics, have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023-2024 winter season, 26 unique MDSS forecast data attributes were collected at 0, 1, 3, 6, 12 and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024 and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increases the robustness of post-storm performance analysis and allows road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations as well as training tools for winter operation stakeholders in Indiana and beyond.

Decadal Forecasts of Large Earthquakes along the Northern San Andreas Fault System, California: Increased Activity on Regional Creeping Faults Prior to Major and Great Events

The three largest earthquakes in northern California since 1849 were preceded by increased decadal activity for moderate-size shocks along surrounding nearby faults. Increased seismicity, double-difference precise locations of earthquakes since 1968, geodetic data and fault offsets for the 1906 great shock are used to re-examine the timing and locations of possible future large earthquakes. The physical mechanisms of regional faults like the Calaveras, Hayward and Sargent, which exhibit creep, differ from those of the northern San Andreas, which is currently locked and is not creeping. Much decadal forerunning activity occurred on creeping faults. Moderate-size earthquakes along those faults became more frequent as stresses in the region increased in the latter part of the cycle of stress restoration for major and great earthquakes along the San Andreas. They may be useful for decadal forecasts. Yearly to decadal forecasts, however, are based on only a few major to great events. Activity along closer faults like that in the two years prior to the 1989 Loma Prieta shock needs to be examined for possible yearly forerunning changes to large plate boundary earthquakes. Geodetic observations are needed to focus on identifying creeping faults close to the San Andreas. The distribution of moderate-size earthquakes increased significantly since 1990 along the Hayward fault but not adjacent to the San Andreas fault to the south of San Francisco compared to what took place in the decades prior to the three major historic earthquakes in the region. It is now clear from a re-examination of the 1989 mainshock that the increased level of moderate-size shocks in the one to two preceding decades occurred on nearby East Bay faults. Double-difference locations of small earthquakes provide structural information about faults in the region, especially their depths. The northern San Andreas fault is divided into several strongly coupled segments based on differences in seismicity.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Growth and Interactions of Multi-Source Perturbations in Convection-Allowing Ensemble Forecasts

This study investigated the growth of forecast errors stemming from initial conditions(ICs),lateral boundary conditions(LBCs),and model(MO)perturbations,as well as their interactions,by conducting seven 36 h convectionallowing ensemble forecast(CAEF)experiments.Two cases,one with strong-forcing(SF)and the other with weak-forcing(WF),occurred over the Yangtze-Huai River basin(YHRB)in East China,were selected to examine the sources of uncertainties associated with perturbation growth under varying forcing backgrounds and the influence of these backgrounds on growth.The perturbations exhibited distinct characteristics in terms of temporal evolution,spatial propagation,and vertical distribution under different forcing backgrounds,indicating a dependence between perturbation growth and forcing background.A comparison of the perturbation growth in different precipitation areas revealed that IC and LBC perturbations were significantly influenced by the location of precipitation in the SF case,while MO perturbations were more responsive to convection triggering and dominated in the WF case.The vertical distribution of perturbations showed that the sources of uncertainties and the performance of perturbations varied between SF and WF cases,with LBC perturbations displaying notable case dependence.Furthermore,the interactions between perturbations were considered by exploring the added values of different source perturbations.For the SF case,the added values of IC,LBC,and MO perturbations were reflected in different forecast periods and different source uncertainties,suggesting that the combination of multi-source perturbations can yield positive interactions.In the WF case,MO perturbations provided a more accurate estimation of uncertainties downstream of the Dabie Mountain and need to be prioritized in the research on perturbation development.

Customer Information Disclosure and Analyst Forecasts : Empirical Evidence from Analysts' Perspective

Based on the company's disclosure of key customer information,the impact of corporate customer concentration on analyst forecast was studied,and we further studied the impact of detailed customer names on analyst forecasts. It is found that:(i) customer concentration significantly affects the accuracy of analyst forecasts. The higher the customer concentration is,the lower the accuracy of analyst forecasts is;(ii) Voluntary disclosure of customer names can provide incremental information to analysts and mitigate the negative impact of customer concentration on the accuracy of analyst forests;(iii) further research has found that the incremental information brought by the state-owned enterprises' disclosure of the customer names to analysts is more obvious; disclosure of customer names by companies with high environmental uncertainty is more likely to be of concern to analysts; and star analysts have a higher ability to interpret customer names than non-star analysts.

New European Union＇s Requirements and IFRS Practice Statement ＂Management Commentary＂： Does MD＆A Disclosure Quality Affect Analysts＇ Forecasts？

The Management Discussion and Analysis （MD＆A） is a mandatory document under the European Union＇s （EU） law. In 2003, the EU issued Directive 2003/51/EC, which broadened the information that firms have to provide in their MD＆A, and in 2010 the International Accounting Standards Board （IASB） issued the International Financial Reporting Standards （IFRS） Practice Statement ＂Management Commentary＂, a non-binding guidance for the presentation of this document. The aim of this paper is to examine the relationship between MD＆A disclosure quality and properties of analysts＇ forecasts. In fact, although most studies found that financial analysts mainly refer to financial statement data in forecasting earnings, there are few researches highlighting the importance of MD＆A disclosures for financial analysts. On this basis, Ramnath, Rock, and Shane （2008） called for researches in order to better understand the relationship between the information really used by analysts and their forecasts. To assess the quality of MD＆A disclosures, we developed a multidimensional measure on the basis of the EU requirements and the IFRS Practice Statement, and then we regressed this variable on both forecast accuracy and dispersion. The findings show that our measure of MD＆A disclosure quality is significantly and positively related to forecast accuracy. We conducted other analyses in order to better understand the previous relationship and we found that, if we analyze the different information contained in the MD＆A statement, financial analysts consider useful accounting and financial data in forecasting earnings. These results enhance our understanding of the role of MD＆A disclosures in the wide set of information that firms provide to financial statement users.

Earnings disaggregation and analysts＇ forecasts

Accounting concepts dictate that separately disclosed components should contain separate useful information. This paper examines the relations between income statement components and analysts＇ earnings forecasts and forecast errors. Regressions explaining earnings forecasts using earnings components provide a better fit than regression using just aggregate income to explain forecasts. We interpret this as consistent with the hypothesis that analysts use incremental information in components not available in aggregate income. However, additional tests based on predictability of forecast errors indicate that analysts do not incorporate all information available in components into earnings forecasts. In addition, this inefficiency appears to increase at longer forecast horizons.

Application Research of Temperature Forecasts on Elman Neural Network

[Objective] The aim was to establish Elman neural network model to predict the dynamic changes of temperature. [Method] Considering the inherent nature of temperature, and dy dint of the temperature in Chongqing during 1951-2010, the Elman artificial neural network model was applied to predict the temperature. [Result] This simulation result suggested that the relative error was small and can have a good simulation to the future temperature changes. [Conclusion] The prediction result can guide agricultural production and further apply to the field of pricing the weather derivative products.

Applications of Bias-removed Ensemble Mean in the Gale Forecasts over the Yellow Sea and the Bohai Sea

Based on the daily sea surface wind field prediction data of Japan Meteorological Agency(JMA) forecast model,National Centers for Environmental Prediction(NCEP GFS) model and U.S.Navy Operational Global Atmospheric Prediction System(NOGAPS) model at 12:00 UTC from June 28 to August 10 in 2009,the bias-removed ensemble mean(BRE) was used to do the forecast test on the sea surface wind fields,and the root-mean-square error(RMSE) was used to test and evaluate the forecast results.The results showed that the BRE considerably reduced the RMSEs of 24 and 48 h sea surface wind field forecasts,and the forecast skill was superior to that of the single model forecast.The RMSE decreases in the south of central Bohai Sea and the middle of the Yellow Sea were the most obvious.In addition,the BRE forecast improved evidently the forecast skill of the gale process which occurred during July 13-14 and August 7 in 2009.The forecast accuracy of the wind speed and the gale location was also improved.

Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations

This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error.

Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF

The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution.

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from E1 Nifio to Yangtze River basin rainfall meant that the strong E1 Nifio in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.

A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area

In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.

Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method

A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.

Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: a case study

A regional Arctic configuration of the Massachusetts Institute of Technology general circulation model （MIT-gcm） is used as the coupled ice-ocean model for forecasting sea ice conditions in the Arctic Ocean at the Na-tional Marine Environmental Forecasting Center of China （NMEFC）, and the numerical weather prediction from the National Center for Environmental Prediction Global Forecast System （NCEP GFS） is used as the atmospheric forcing. To improve the sea ice forecasting, a recently developed Polar Weather Research and Forecasting model （Polar WRF） model prediction is also tested as the atmospheric forcing. Their forecasting performances are evaluated with two different satellite-derived sea ice concentration products as initializa-tions： （1） the Special Sensor Microwave Imager/Sounder （SSMIS） and （2） the Advanced Microwave Scanning Radiometer for EOS （AMSR-E）. Three synoptic cases, which represent the typical atmospheric circulations over the Arctic Ocean in summer 2010, are selected to carry out the Arctic sea ice numerical forecasting experiments. The evaluations suggest that the forecasts of sea ice concentrations using the Polar WRF atmo-spheric forcing show some improvements as compared with that of the NCEP GFS.

The Impact of AIRS Atmospheric Temperature and Moisture Profiles on Hurricane Forecasts: Ike(2008) and Irene(2011)

Atmospheric InfraRed Sounder （AIRS） measurements are a valuable supplement to current observational data,especially over the oceans where conventional data are sparse.In this study,two types of AIRS-retrieved temperature and moisture profiles,the AIRS Science Team product （SciSup） and the single field-of-view （SFOV） research product,were evaluated with European Centre for Medium-Range Weather Forecasts （ECMWF） analysis data over the Atlantic Ocean during Hurricane Ike （2008） and Hurricane Irene （2011）.The evaluation results showed that both types of AIRS profiles agreed well with the ECMWF analysis,especially between 200 hPa and 700 hPa.The average standard deviation of both temperature profiles was approximately 1 K under 200 hPa,where the mean AIRS temperature profile from the AIRS SciSup retrievals was slightly colder than that from the AIRS SFOV retrievals.The mean SciSup moisture profile was slightly drier than that from the SFOV in the mid troposphere.A series of data assimilation and forecast experiments was then conducted with the Advanced Research version of the Weather Research and Forecasting （WRF） model and its three-dimensional variational （3DVAR） data assimilation system for hurricanes Ike and Irene.The results showed an improvement in the hurricane track due to the assimilation of AIRS clear-sky temperature profiles in the hurricane environment.In terms of total precipitable water and rainfall forecasts,the hurricane moisture environment was found to be affected by the AIRS sounding assimilation.Meanwhile,improving hurricane intensity forecasts through assimilating AIRS profiles remains a challenge for further study.

The Impact of Dropsonde Data on Forecasts of Hurricane Debby by the Meteorological Office Unified Model

The numerical product of hurricane tracks vastly depends on initial observation fields. However, the forecast error is very large because of lack of observational data, especially when hurricanes are over the sea. This paper shows that extra non-real-time data (dropsonde data) can improve hurricane track forecasts compared with real-time observational data, and that the wind and relative humidity components of the dropsonde data have the greatest impact on the track forecasts.

Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

The application of numerical weather prediction （NWP） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous （yes/no）, and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCER UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.