Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Objective and probabilistic long-range forecasts of summertime air temperatures in South Korea based on Gaussian processes

We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multi-model ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with pre-determined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June (Heidke skill score; HSS = 0.46, 0.72, and 0.16 for mean temperature) and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

Increased occurrence of high impact compound events under climate change

While compound weather and climate events (CEs) can lead to significant socioeconomic consequences, their response to climate change is mostly unexplored. We report the first multi-model assessment of future changes in return periods for the co-occurrence of heatwaves and drought, and extreme winds and precipitation based on the Coupled Model Intercomparison Project (CMIP6) and three emission scenarios. Extreme winds and precipitation CEs occur more frequently in many regions, particularly under higher emissions. Heatwaves and drought occur more frequently everywhere under all emission scenarios examined. For each CMIP6 model, we derive a skill score for simulating CEs. Models with higher skill in simulating historical CEs project smaller increases in the number of heatwaves and drought in Eurasia, but larger numbers of strong winds and heavy precipitation CEs everywhere for all emission scenarios. This result is partly masked if the whole CMIP6 ensemble is used, pointing to the considerable value in further improvements in climate models.

Bias Correction Method of High-Resolution Satellite-Based Precipitation Product for Peninsular Malaysia

Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R2) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.

Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra

Accurate high-resolution precipitation forecasts are critical yet challenging for weather prediction under complex topography or severe synoptic forcing. Data fusion and assimilation aimed at improving model forecasts, as one possible approach, has gained increasing attention in past decades. This study investigates the influence of the observations from a C-band Doppler radar over the west coast of Sumatra on high-resolution numerical simulations of precipitation around its vicinity under the Madden–Julian oscillation (MJO) in January and February 2018. Cases during various MJO phases were selected for simulations with an advanced research version of the weather research and forecasting (WRF) model at a cloud-permitting scale (~3 km). A 3-dimensional variational (3DVAR) data assimilation method and a hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) method, based on the NCEP Gridpoint Statistical Interpolation (GSI) assimilation system, were used to assimilate the radar reflectivity and the radial velocity data. The WRF-simulated precipitation was validated with the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data, and the fractions skill score (FSS) was calculated in order to evaluate the radar data impacts objectively. The results show improvements in the simulated precipitation with hourly radar data assimilation 6 h prior to the simulations. The modifications with assimilation were validated through the observation departure and moist convection. It was found that forecast improvements are relatively significant when precipitation is more related to local-scale convection but rather small when the background westerly wind is strong under the MJO active phase. The additional simulation experiments, under a 1- or 2-day assimilation cycle, indicate better improvements in the precipitation simulation with 3DEnVAR radar assimilation than those with the 3DVAR method.

Flexible and consistent quantile estimation for intensity–duration–frequency curves

Assessing the relationship between the intensity, duration, and frequency (IDF) of extreme precipitation is required for the design of water management systems. However, when modeling sub-daily precipitation extremes, there are commonly only short observation time series available. This problem can be overcome by applying the duration-dependent formulation of the generalized extreme value (GEV) distribution which fits an IDF model with a range of durations simultaneously. The originally proposed duration-dependent GEV model exhibits a power-law-like behavior of the quantiles and takes care of a deviation from this scaling relation (curvature) for sub-hourly durations (Koutsoyiannis et al., 1998). We suggest that a more flexible model might be required to model a wide range of durations (1 min to 5 d). Therefore, we extend the model with the following two features: (i) different slopes for different quantiles (multiscaling) and (ii) the deviation from the power law for large durations (flattening), which is newly introduced in this study. Based on the quantile skill score, we investigate the performance of the resulting flexible model with respect to the benefit of the individual features (curvature, multiscaling, and flattening) with simulated and empirical data. We provide detailed information on the duration and probability ranges for which specific features or a systematic combination of features leads to improvements for stations in a case study area in the Wupper catchment (Germany). Our results show that allowing curvature or multiscaling improves the model only for very short or long durations, respectively, but leads to disadvantages in modeling the other duration ranges. In contrast, allowing flattening on average leads to an improvement for medium durations between 1 h and 1 d, without affecting other duration regimes. Overall, the new parametric form offers a flexible and enhanced performance model for consistently describing IDF relations over a wide range of durations, which has not been done before as most existing studies focus on durations longer than 1 h or day and do not address the deviation from the power law for very long durations (2–5 d).

Skalenabhängigkeit der Niederschlagsprognosengüte in einem globalen Wettervorhersagemodell

<p>Aus Satellitendaten abgeleitete Niederschlagsbeobachtungen (GPM-IMERG) werden verwendet, um die Güte der operationellen ECMWF Niederschlagsprognose zu evaluieren. Der Vorteil von Satellitendaten gegenüber der klassichen Niederschlagsbeobachtung an Bodenstationen ist die flächendeckende Verfügbarkeit, sowie der geringere Repräsentationsfehler beim Vergleich mit Gitterdaten eines Modells. Der Hauptnachteil ist die quantitative Unsicherheit aufgrund der deutlich indirekteren Bestimmung des Niederschlags, die bei der Verifikation berücksichtigt werden muss. Das in der Prognosen-Evaluierung betrachtete Gebiet erstreckt sich von 60<sup>o</sup>N bis 60<sup>o</sup>S, wobei die Auflösung der GPM-IMERG Daten 0.1<sup>o</sup>x0.1<sup>o</sup> beträgt. Die verwendete Metrik ist der Fractions Skill Score (FSS), sowie ein äquivalentes Maß für Ensembleprognosen. Es werden Skalen von etwa 10 km (Maschenweite des Modellgitters in der deterministischen Prognose) bis etwa 200 km betrachtet. Für Tagessummen des Niederschlags wird die Skalenabhängigkeit des Prognosefehlers in den Tropen und in mittleren Breiten als Funktion des Prognosehorizonts bestimmt. Jahreszeitliche Unterschiede, sowie Unterschiede zwischen Meeresgebieten und kontinentalen Gebieten werden analysiert. In Abhängigkeit von der Intensität des Niederschlags werden Grenzen der Vorhersagbarkeit ermittelt, wobei zusätzlich zur Gesamtstatistik auch einzelne Starkniederschlagsereignisse betrachtet werden. Die Ergebnisse liefern das bisher umfassendste Bild der aktuellen Güte der ECMWF Niederschlagsprognose. Darüber hinaus wird die zeitliche Entwicklung der Prognosequalität in den letzten 10 Jahren dokumentiert und untersucht, inwieweit einzelne Verbesserungen spezifischen Modelländerungen (Auflösung, Modellphysik) zugeordnet werden können.</p>

Verification and usability of location specific medium range weather forecast for Kullu valley

Weather plays a crucial role in agriculture. Precipitation, temperature, humidity, wind speed and direction, drying conditions, dry and wet spells are the most important weather elements information about whom could play a significant role in farm planning and operations. Inclement weather events like drought and floods, cold and heat waves, hails, squalls, tropical storms severely affect the production. Occurrences of erratic weather are beyond human control. It is possible to adapt or mitigate their malevolent effect to some extend if the occurrence of the events is predicted well in advance and farmers are suitably advised to take ameliorative measures. Attempts were made to verify the weather forecasts received on every Tuesday and Friday from NCMRWF/IMD. The verification analysis was carried out on weekly, seasonal and annual basis using various verification techniques, viz., Ratio Score (RS), Critical Success Index (CSI), Heidke Skill Score (HSS), Hanssen and Kuipers Score (HK), Root Mean Square Error (RMSE), usability analysis and correlation approach during 2000-01 to 2009-10. The analysis depicted that ratio score on yearly basis was highest (74.6) during 2005-06 followed by 2004-05 (72.9) and 2003-04 (72.7). The value of H.K. score ranged between 24 and 42. The forecast found within quite usability range for most of the parameters but improvements are still possible. The correlation analysis showed that there was high correlation between observed and predicted values over the years. Hence, the forecast was found widely applicable among different user groups.

Kombination von NWV- und Nowcasting-Ensembles zur Verbesserung der Vorhersagen konvektiven Starkniederschlags des DWD im Rahmen von SINFONY

<p>Verlässliche Niederschlagsvorhersagen innerhalb des Kürzestfristbereichs sind unerlässlich für präzise Warnungen und können die Vorlaufzeit für Entscheidungsträger im Bereich der Gefahrenabwehr und des Rettungswesens erhöhen. In der operationellen Wettervorhersage beruhen Vorhersage und Warnung vor konvektivem Starkniederschlag innerhalb der ersten zwei Stunden auf radarbasierten Nowcastingverfahren, während für spätere Zeitpunkte Simulationen konvektionserlaubender Ensemblevorhersagesysteme genutzt werden.</p> <p>Im Rahmen des Projekts SINFONY (Seamless INtegrated FOrecastiNg sYstem) des Deutschen Wetterdienstes wird ein integriertes Ensemblesystem auf konvektiver Skala im Bereich der Kürzestfristvorhersage entwickelt. Um die optimale Kombination der bisher unabhängigen Systeme von Nowcasting und numerischer Wetterverhorsage zu erleichtern, wurde mit STEPS-DWD eine Adaption des weitverbreiteten STEPS (u.a. Seed 2003, Bowler et al., 2006) als Nowcast-Ensemble in den Testbetrieb überführt. Basis der NWV ist ICON-D2-RUC, welches derzeit stündlich initialisiert  Ensemblevorhersagen bis +8h Stunden mit einer horizontalen Auflösung von 2,2km liefert. Kernkomponenten dieser Modellversion sind die Nutzung eines Zwei-Momenten-Mikrophysikschemas sowie die zusätzliche Assimilation von hochaufgelösten Fernerkundungsdaten wie 3D-Radardaten und Meteosat-SEVIRI-Daten.</p> <p>Auf Basis der zwei vorangenannten Ensemblesysteme STEPS-DWD und ICON-D2-RUC werden zwei Methoden zur Kombination der Vorhersagen dieser Systeme präsentiert. In einem ersten Verfahren wird die Methode nach Nerini et al., 2019 adaptiert. Hierbei werden die Vorhersagen von Reflektivitäten und Regenraten im physischen Raum auf Basis eines Ensemble-Kalmanfilters kombiniert. Durch eine zeitlich und räumliche Auflösung von fünf Minuten bzw. 1x1km wird unter Beibehaltung eines realistischen Aussehens der Niederschlagssysteme eine Möglichkeit zur Abschätzung der weiteren Entwicklung bis +6h geschaffen.<br /><br />Weiterhin wird eine neue statistische Methode vorgestellt, mit der prognostizierte Niederschlagssummen auf Basis Neuronaler Netze (NN) im Wahrscheinlichkeitsraum kombiniert werden (vgl. Schaumann et al., 2021). Ziel ist es, mit einem Training sowohl nahtlose und kalibrierte Vorhersagen zu erhalten, als auch konsistente Überschreitungswahrscheinlichkeiten gegenüber allen Schwellwerten zu erreichen. Für die Optimierung wurden drei Datensätze von jeweils drei Monaten verwendet, wobei die Datensätze A & B Ensemble-MOS und RadVOR mit einer jeweiligen horizontalen Auflösung von 20km beinhalten. In Datensatz C werden Vorhersagen eines dreistündig initialisierten ICON-D2-RUC sowie STEPS-DWD mit einer Auflösung von 2,2km verwendet. Die Hyperparameter der NN wurden mit Datensatz A optimiert und die daraus resultierenden NN mittels Rolling Origin Validation auf Datensatz B & C validiert. Hieraus werden Vorhersagen mit einer zeitlichen Auflösung von 1h bis +6h erzeugt.<br /><br />Für beide Verfahren wird durch mehrere Verifikationsmetriken (FSS, Bias, Brier Skill Score, Reliability und Reliability-Diagramm) gezeigt, dass die kombinierten Vorhersagen für alle Vorhersagezeiten gleich oder besser als die der individuellen Systeme sind.</p>