Short-range Forecasts Research Articles

Intercomparison of numerical atmospheric dispersion prediction models for emergency response to emissions of radionuclides with limited source information in the Fukushima Dai-ichi nuclear power plant accident

The utilization of numerical atmospheric dispersion prediction (NDP) models for assisting the emergency response to emission of radionuclides has been recommended by a working group of the Meteorological Society of Japan. This paper verifies the feasibility of the recommendation through NDP model intercomparison with limited emission source information for the case of the Fukushima Dai-ichi Nuclear Power Plant accident caused by the Great East Japan Earthquake in 2011. According to the recommendation of the working group, the NDP models are run under the assumption of a constant rate of emission during the whole forecast period. This is the worst-case scenario when limited source information is available. Generally, no information is provided on the temporal variability and strength of the emissions, while the source location is known. Surface air radionuclide forecasts are utilized for providing warnings of the risk of inhaling radioactive substances suspended in the low-level atmosphere, whereas column-integrated radionuclide forecasts are utilized for estimating the potential maximum wet deposition of radioactive materials on the ground due to precipitation. The NDP model short-range forecasts were validated with observational data for three locations, at the times when the most serious contamination events occurred at each of the three monitoring stations. The NDP models successfully predicted the risk of surface air contamination and/or ground surface contamination caused by wet deposition in these cases. Particularly, the NDP model forecasts allow us to disseminate warnings at effective lead times before exposure to radiation. The different NDP models gradually deviate their forecasts as the lead time progresses. The deviations may indicate the magnitude of forecast errors. Thus, the use of multi-model forecasts is of greater benefit than the single model forecasts, because forecast error information is suggested.

Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.

Variational Bias Correction of TAMDAR Temperature Observations in the WRF Data Assimilation System

AbstractA variational bias correction (VarBC) scheme is developed and tested using regional Weather Research and Forecasting Model Data Assimilation (WRFDA) to correct systematic errors in aircraft-based measurements of temperature produced by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system. Various bias models were investigated, using one or all of aircraft height tendency, Mach number, temperature tendency, and the observed temperature as predictors. These variables were expected to account for the representation of some well-known error sources contributing to uncertainties in TAMDAR temperature measurements. The parameters corresponding to these predictors were evolved in the model for a two-week period to generate initial estimates according to each unique aircraft tail number. Sensitivity experiments were then conducted for another one-month period. Finally, a case study using VarBC of a cold front precipitation event is examined. The implementation of VarBC reduces biases in TAMDAR temperature innovations. Even when using a bias model containing a single predictor, such as height tendency or Mach number, the VarBC produces positive impacts on analyses and short-range forecasts of temperature with smaller standard deviations and biases than the control run. Additionally, by employing a multiple-predictor bias model, which describes the statistical relations between innovations and predictors, and uses coefficients to control the evolution of components in the bias model with respect to their reference values, VarBC further reduces the average error of analyses and short-range forecasts with respect to observations. The potential impacts of VarBC on precipitation forecasts were evaluated, and the VarBC is able to indirectly improve the prediction of precipitation location by reducing the forecast error for wind-related synoptic circulation leading to precipitation.

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

New Methods and Technologies of Ice Forecasts for the Arctic Seas

Methodological approaches in the area of long- and short-range ice forecasts are discussed. Three basic modern directions of long-term ice forecast development are shown. New methods of long-range ice forecasts developed in the recent decade are presented. The ways of improving the AARI short-range ice forecast method based on the coupled ice-ocean dynamic-thermodynamic model are considered. It is shown that the numerical modeling as a specific branch of ice forecasting requires permanent development. The automation of ice forecasts can be implemented as an automated workplace or as a hardware-software complex.

The Convective Rainfall Rate from Cloud Physical Properties Algorithm for Meteosat Second-Generation Satellites: Microphysical Basis and Intercomparisons using an Object-Based Method

The convective rainfall rate from cloud physical properties (CRPh) algorithm for Meteosat second-generation satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT’ Satellite Application Facility in support of nowcasting and very short-range forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for a few hours. This letter critically discusses the theoretical basis of the algorithm with special emphasis on the empirical values and assumptions in the microphysics of precipitation, and compares the qualitative performances of the CRPh with its antecessor, the convective rainfall rate algorithm (CRR), using an object-based method applied to a case-study. The analyses show that AEMET’s CRPh is physically consistent and outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges of improving the product to an all-day algorithm are also presented.

Modeling lightning density using cloud top parameters

The Price-Rind 92 parameterization (Price and Rind, 1992) that utilizes cloud top height as predictor for the estimation of lightning density is widely used by modelers in an attempt to forecast electrical activity in thunderstorms. In the present paper new parameterizations for the estimation of lightning density of convective clouds are formulated. LINET lightning data, NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short-Range Forecasting) products and ERA-Interim (ECMWF-Re Analysis) data, covering the summer of 2016 over continental Europe, are used. The proposed models estimate the lightning density of convective clouds, using cloud top height, cloud top pressure and cold cloud depth as predictors. Model efficiency statistics calculated over an independent dataset, suggest that the proposed models can be considered successful over the specific area (continental Europe) and period (summer). The new cloud top height model differs from the PR92 and other parameterizations, which is not an unexpected result, since every model has its own characteristics, strengths and weaknesses. The new parameterizations could be utilized in numerical model simulations to produce quantitative estimations of the amount of strokes over convective areas.

Study on the Construction of Initial Condition Perturbations for the Regional Ensemble Prediction System of North China

The regional ensemble prediction system (REPS) of North China is currently under development at the Institute of Urban Meteorology, China Meteorological Administration, with initial condition perturbations provided by global ensemble dynamical downscaling. To improve the performance of the REPS, a comparison of two initial condition perturbation methods is conducted in this paper: (i) Breeding, which was specifically designed for the REPS, and (ii) Dynamical downscaling. Consecutive tests were implemented to evaluate the performances of both methods in the operational REPS environment. The perturbation characteristics were analyzed, and ensemble forecast verifications were conducted. Furthermore, a heavy precipitation case was investigated. The main conclusions are as follows: the Breeding perturbations were more powerful at small scales, while the downscaling perturbations were more powerful at large scales; the difference between the two perturbation types gradually decreased with the forecast lead time. The downscaling perturbation growth was more remarkable than that of the Breeding perturbations at short forecast lead times, while the perturbation magnitudes of both schemes were similar for long-range forecasts. However, the Breeding perturbations contained more abundant small-scale components than downscaling for the short-range forecasts. The ensemble forecast verification indicated a slightly better downscaling ensemble performance than that of the Breeding ensemble. A precipitation case study indicated that the Breeding ensemble performance was better than that of downscaling, particularly in terms of location and strength of the precipitation forecast.

A Prediction Scheme for Daily Maximum and Minimum Temperature Forecasts Using Recurrent Neural Network and Rough set

A new nonlinear objective prediction scheme has been developed for predicting 24h daily maximum and minimum temperature forecasts at 14 stations in Guangxi, China during Jan, 2015-Jun, 2018 using Recurrent Neural Network (RNN) and based on the daily average, maximum, minimum temperature and precipitation data. Taking the climatology and persistence predictors as primary factors, the conditional attribute reduction method of rough set theory is adopted. By eliminating the unrelated attributes, the predictors direct correlated with the predictand (maximum and minimum temperature) are taken as the RNN model input by means of attribute reduction. This new scheme is validated with 24h short-range forecasts spanning Jan to Jun, 2018. Using identical predictors and sample cases, predictions of the RNN model are compared with the stepwise regression method, and results show that the former is more accurate. The mean absolute errors of RNN at 14 stations in Guangxi are lower than those of the stepwise regression method. The mean forecast accuracy with absolute errors being less than 2°C (1°C) of RNN is higher than that of the stepwise regression method. Moreover, the number of forecast errors larger than 2°C and the system deviation of daily maximum (minimum) temperature prediction are significantly reduced by RNN model, indicating a potentially better operational weather prediction tool.

Nonlinear Wave Ensemble Averaging in the Gulf of Mexico Using Neural Networks

AbstractArtificial neural networks (ANNs) applied to nonlinear wave ensemble averaging are developed and studied for Gulf of Mexico simulations. It is an approach that expands the conservative arithmetic ensemble mean (EM) from the NCEP Global Wave Ensemble Forecast System (GWES) to a nonlinear mapping that better captures the differences among the ensemble members and reduces the systematic and scatter errors of the forecasts. The ANNs have the 20 members of the GWES as input, and outputs are trained using observations from six buoys. The variables selected for the study are the 10-m wind speed (U10), significant wave height (Hs), and peak period (Tp) for the year of 2016. ANNs were built with one hidden layer using a hyperbolic tangent basis function. Several architectures with 12 different combinations of neurons, eight different filtering windows (time domain), and 100 seeds for the random initialization were studied and constructed for specific forecast days from 0 to 10. The results show that a small number of neurons are sufficient to reduce the bias, while 35–50 neurons produce the greatest reduction in both the scatter and systematic errors. The main advantage of the methodology using ANNs is not on short-range forecasts but at longer forecast ranges beyond 4 days. The nonlinear ensemble averaging using ANNs was able to improve the correlation coefficient on forecast day 10 from 0.39 to 0.61 for U10, from 0.50 to 0.76 for Hs, and from 0.38 to 0.63 for Tp, representing a gain of five forecast days when compared to the EM currently implemented.

О роли синоптика в технологии подготовки краткосрочных прогнозов погоды

О роли синоптика в технологии подготовки краткосрочных прогнозов погоды

Assimilation of MWHS and MWTS radiance data from the FY-3A satellite with the POD-3DEnVar method for forecasting heavy rainfall

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun-3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.

Better Baltic Sea wave forecasts: improving resolution or introducing ensembles?

Abstract. The performance of short-range operational forecasts of significant wave height (SWH) in the Baltic Sea is evaluated. Forecasts produced by a base configuration are intercompared with forecasts from two improved configurations: one with improved horizontal and spectral resolution and one with ensembles representing uncertainties in the physics of the forcing wind field and the initial conditions of this field. Both of the improved forecast classes represent an almost equal increase in computational costs. Therefore, the intercomparison addresses the question of whether more computer resources would be more favorably spent on enhancing the spatial and spectral resolution or, alternatively, on introducing ensembles. The intercomparison is based on comparisons with hourly observations of significant wave height from seven observation sites in the Baltic Sea during the 3-year period from 2015 to 2017. We conclude that for most wave measurement sites, the introduction of ensembles enhances the overall performance of the forecasts, whereas increasing the horizontal and spectral resolution does not. These sites represent offshore conditions, in that they are well exposed from all directions, are a large distance from the nearest coast and in deep water. Therefore, there is the a priori expectation that a detailed shoreline and bathymetry will not have any impact. Only at one site do we find that increasing the horizontal and spectral resolution significantly improves the forecasts. This site is situated in nearshore conditions, close to land and a nearby island, and is therefore shielded from many directions. Consequently, this study concludes that to improve wave forecasts in offshore areas, ensembles should be introduced. For near shore areas, in comparison, the study suggests that additional computational resources should be used to increase the resolution.

Model Development and Hindcast Simulations of NOAA’s Integrated Northern Gulf of Mexico Operational Forecast System

NOAA’s National Ocean Service is upgrading three existing northern Gulf of Mexico (GOM) operational nowcast/forecast systems (OFS) by integrating them into one single system (INGOFS) and developing additional domain coverage to encompass the lower Mississippi River, Lake Pontchartrain, Texas coastal embayments, and Mexican coastal waters. The system will produce real-time nowcast and short-range forecast guidance for water levels, 3-dimensional currents, water temperature, and salinity. INGOFS will be implemented using the Finite Volume Community Ocean Model (FVCOM). This paper describes the model configuration and results from a one-year (2 August 2016–1 August 2017) hindcast simulation. The model grid is composed of about 300,000 nodes and 600,000 elements, and has a spatial resolution ranging from 45 m near the coast to around 10 km on the open ocean boundary. It uses the FVCOM wetting and drying feature, the quadratic bottom friction scheme, and the two-equation model of the Mellor-Yamada Level 2.5 turbulence closure scheme. The hindcast results of water levels, surface temperatures, and salinity were verified by comparing the model time series with in situ observations. The root-mean-squared errors are about 0.08 m for water levels, about 1.1 °C for temperatures, and about 3.7 psu for salinity. The hindcast configuration will be further tested in a nowcast/forecast environment for a one-year period. The upgraded system is anticipated to be in operational production in mid-2020.

Potential of an EnKF Storm-Scale Data Assimilation System Over Sparse Observation Regions with Complex Orography

High-impact weather events over sparse data regions with complex orography, such as the Mediterranean region, remain a challenge for numerical weather prediction. This study evaluates, for the first time, the ability of a multiscale ensemble-based data assimilation system to reproduce a heavy precipitation episode that occurred during the first Special Observation Period (SOP1) of the Hydrological cycle in the Mediterranean Experiment (HyMeX). During the Intense Observation Period (IOP13) from 14 to 15 October 2012, convective maritime activity associated with an advancing cold front affected coastal areas of southern France, Corsica and Italy. With the main objective of improving forecasts of this weather event, a data assimilation (DA) system using the Ensemble Kalman Filter (EnKF) algorithm is implemented. The potential impact of assimilating conventional in-situ observations (METAR, aircrafts, buoys and rawinsondes) and single-Doppler reflectivity data to improve numerical representation of growing convective maritime structures that will evolve towards coastal populated areas is evaluated. Results indicate that information provided by both observation sources contribute to initiation and subsequent evolution of convective structures not captured by the conventional runs. Notably, data assimilation experiments produce the best quantitative verification scores for the short range (6–8 h) forecasts of accumulated precipitation. Beyond 6–8 h, data assimilation experiments and those without data assimilation are indistinguishable. Sensitivity experiments, evaluating the impact of increasing the length of the radar data assimilation period, reveal the importance of assimilating high-frequency reflectivity data during a mid-term period (6 h approx.) to better depict deep convective structures initiated over the sea that evolve towards populated coastal areas.

Agrometeorology in Croatia

ABSTRACTMeteorological and Hydrological Service was established 70 years ago and agrometeorological activities (soil temperature measurements and phenological observations) started in 1951. Scientific research is divided into an agricultural meteorology, forest meteorology and plant phenology. The basic purpose of research is the impact of regional climate change on the crop production, on the phenological stages of different plants as forest trees and shrubs, fruit-trees, olive-trees, grapevine and maize, and on the potential wildfire risk. In the operational tasks short-range and medium-range forecasts have been produced for TV and internet since 2004, radio since 1990 and specialized journals since 1988. For further development of Croatian agrometeorology, future plans are using monthly and seasonal forecasts for prediction of the development of yield of major agricultural crops, for irrigation purposes, as well as for potential risk of wildfires and applying satellite data and nowcasting for warning purposes in agriculture and in forest protection against fire.

To the Problem of Verification of Methods for Short-range Forecasting of Hydrological Parameters

The main principles of the existing methodology for the verification of forecasting techniques in the absence of the sample volume impact on the accuracy of determination of statistical parameters are considered. A method is proposed for the verification, comparison, and selection of forecasting methods. The method is based on the persistence of the predicted phenomenon and allows creating optimum forecast schemes with account of priority of expected parameters of accuracy. It is anticipated that the obtained results will find application in the practice of hydrological forecasting.

Riding waves of intra-seasonal demand in surf tourism: analysing the nexus of seasonality and 21st century surf forecasting technology

ABSTRACTThis study examines the natural component of seasonality in surf tourism. Using survey responses collected globally, we first analyse how surfers report using forecasts to make travel decisions. Occupancy and head-count data were also collected in Pavones, Costa Rica and analyzed against surf forecast data to empirically assess intra-seasonal fluctuations. Despite a small and highly skilled subset of the surf population, most international travel is booked months in advance based on climatic factors. Case study results indicate, however, that intra-seasonal travel behaviour within a country is correlated to both mid-range (occupancy) and short range (head-counts in the water) forecasts. Combined, surf forecasts and observed natural conditions impact surf travel behaviours in nuanced ways that deserve further attention to better understand destination and hospitality management, as well as, host and guest satisfaction and how they relate to intra-seasonal and intra-regional travel patterns.

Evaluation and Assimilation of the Microwave Sounder MWHS-2 Onboard FY-3C in the ECMWF Numerical Weather Prediction System

This paper presents an evaluation of the quality of data from the MicroWave Humidity Sounder-2, flown on the FY-3C polar orbiting satellite, and first results of trials assimilating the data in all-sky conditions in the European Centre for Medium Range Weather Forecasts’ assimilation system. This instrument combines traditional microwave humidity sounding capabilities at 183 GHz with new channels at 118 GHz, which have never before been available from space. The aim of this paper is twofold: to contribute to the calibration/validation of this satellite by evaluating the data and to test for the first time the impact of assimilating 118-GHz sounding channels in a global numerical weather prediction system. MWHS-2 was first evaluated by comparing observation minus short-range forecast statistics to similar instruments, which indicated that the quality was generally similar. Adding the 118- and 183-GHz channels to the full operational observing system led to small improvements in the short-range (~12 h) forecast accuracy for both sets of channels, and improvements in the 2–4-day wind forecast accuracy for the 183-GHz channels. Short-range forecasts were improved for global humidity in particular when assimilating the 183-GHz channels (0.5% improvement) and for cloud and low-level wind in the Southern Hemisphere extra-tropics when assimilating the 118-GHz channels (by, respectively, 0.5% and 0.2%). In the absence of other atmospheric observations, assimilating the 118-GHz channels improved the global forecast accuracy, but the overall impact was less than for the 183-GHz channels, or for equivalent Advanced Microwave Sounding Unit-A channels.

Neural network cloud top pressure and height for MODIS

Abstract. Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE.