Suchergebnisse

Abstract. This study aims at simulating satellite-measured lightning observations with numerical weather prediction (NWP) system variables. A total of eight parameters, calculated with the AROME-France NWP system variables, were selected from a literature review to be used as proxies for satellite lightning observations. Two different proxy types emerged from this literature review: microphysical and dynamical proxies. Here, we investigate which ones are best related to satellite lightning and calibrate an empirical relationship between the best parameters and lightning data. To obtain those relationships, we fit machine learning regression models to our data. In this study, pseudo flash extent accumulation (FEA) observations are used because no actual geostationary lightning observations are available yet over France, and non-geostationary satellite lightning data represent a sample that is too small for our study. The performances of each proxy and machine learning regression model are evaluated by computing fractions skill scores (FSSs) with respect to observed FEA and proxy-based FEA. The present study suggests that microphysical proxies are more suited than the dynamical ones to model satellite lightning observations with the AROME-France NWP system. The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study, but no proxy combination yielded better results than microphysical proxies alone. Finally, different accumulation periods of the FEA had little influence, i.e. similar FSS, on the regression model's ability to reproduce the observed FEA. In future studies, the microphysical-based relationship will be used as an observation operator to perform satellite lightning data assimilation in storm-scale NWP systems and applied to NWP forecasts to simulate satellite lightning data.

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

Abstract. The northeast coast of North America is frequently hit by severe ice storms. These freezing rain events can produce large ice accretions that damage structures, frequently power transmission and distribution infrastructure. For this reason, it is highly desirable to model and forecast such icing events, so that the consequent damages can be prevented or mitigated. The case study presented in this paper focuses on the March 2010 ice storm event that took place in eastern Newfoundland. We apply a combination of a numerical weather prediction model and an ice accretion algorithm to simulate a forecast of this event. The main goals of this study are to compare the simulated meteorological variables to observations, and to assess the ability of the model to accurately predict the ice accretion load for different forecast horizons. The duration and timing of the freezing rain event that occurred between the night of 4 March and the morning of 6 March was simulated well in all model runs. The total precipitation amounts in the model, however, differed by up to a factor of two from the observations. The accuracy of the model air temperature strongly depended on the forecast horizon, but it was acceptable for all simulation runs. The simulated accretion loads were also compared to the design values for power delivery structures in the region. The results indicated that the simulated values exceeded design criteria in the areas of reported damage and power outages.

International audience Water budgets from European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim and National Centers for Environmental Prediction (NCEP) Reanalysis I and II are intercompared and compared to GPS precipitable water and to the 6 year hybrid budget data set described in part 1 of this study. Deficiencies are evidenced in the reanalyses which are most pronounced over the Sahel. Results from operational models (ECMWF Integrated Forecast System, NCEP Global Forecast System, and ARPEGE-Tropiques) and the special ECMWF African Monsoon Multidisciplinary Analyses reanalysis confirm and help understanding these findings. A bias (∼1-2 mm d−1) in precipitation and evapotranspiration leads to an unrealistic view of West Africa as a moisture source during the summer. North of the rainband (13°N-16°N), moisture flux convergence (MFC) shows a minimum in the NCEP models and divergence in the ECMWF models not consistent with the hybrid data set. This feature, added to presence of a deep layer of northerly dry air advected at midlevels (800-400 hPa), is thought to block the development of deep convection in the models and the northward propagation of the monsoonal rainband. The northerly flow is part of a shallow meridional circulation that is driven by the Saharan heat low. This circulation appears too strong in some of the models, a possible consequence of the too-approximate representation of physical processes and land surface properties over the Sahel. In most of the models, evapotranspiration shows poor connection with precipitation. This is linked with large analysis increments in precipitable water, soil moisture, and MFC. Despite the large biases affecting the water budget components in the models, temporal variations (seasonal and interannual) might nevertheless be recovered with reasonable accuracy.

Abstract. Our study is aimed at estimating the added value provided by Numerical Weather Prediction (NWP) data for the modelling and prediction of rainfall-induced shallow landslides. We implemented a quantitative indirect statistical modelling of such phenomena by using, as input predictors, both geomorphological, geological, climatological information and numerical data obtained by running a limited-area weather model. Two standard statistical techniques are used to combine the predictor variables: a generalized linear model and Breiman's random forests. We tested these models for two rainfall events that occurred in 2011 and 2013 in Tuscany region (central Italy). Modelling results are compared with field data and the forecasting skill is evaluated by mean of sensitivity–specificity receiver operating characteristic (ROC) analysis. In the 2011 rainfall event, the random forests technique performs slightly better than generalized linear model with area under the ROC curve (AUC) values around 0.91 vs. 0.84. In the 2013 rainfall event, both models provide AUC values around 0.7. Using the variable importance output provided by the random forests algorithm, we assess the added value carried by numerical weather forecast. The main results are as follows: (i) for the rainfall event that occurred in 2011 most of the NWP data, and in particular hourly rainfall intensities, are classified as "important" and (ii) for the rainfall event that occurred in 2013 only NWP soil moisture data in the first centimetres below ground is found to be relevant for landslide assessment. In the discussions we argue how these results are connected to the type of precipitation observed in the two events.

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years. © 2015, US Government. ; Peer Reviewed

Extreme heat weather events have received increasing attention and has become of special importance as they can remarkably affect sectors as diverse as public health, energy consumption, water resources, natural biodiversity and agricultural production. In this regard, summer temperatures have become a parameter of essential interest under a framework of a hypothetical increase in the number of intense-heat conditions. Thus, their forecast is a crucial aspect bearing in mind a mitigation of the effects and impacts that these intense-heat situations could produce. The current work tries to reach a better understanding of these sorts of situations that are really common over the Western Mediterranean coast. An extreme heat episode that took place in the Valencia Region in July 2009 is analysed, based on the simulations performed with the Regional Atmospheric Modeling System (RAMS). This event recorded maximum temperatures exceeding 40 °C amply extended over the region besides reaching minimum temperatures up to 25.92 °C. We examine the role of improved skin and soil temperature (ST) initial conditions in the forecast results by means of different modelling and satellite-derived products. The influence of incorporating the Land Surface Temperature (LST) into RAMS is not found to produce a meaningful impact on the simulation results, independently of the resolution of the dataset used in the initial conditions of the model. In contrast, the introduction of the ST in lower levels, not only the skin temperature, has a more marked decisive effect in the simulation. Additionally, we have evaluated the influence of increasing the number of soil levels to spread deeper underground. This sensitivity experiment has revealed that more soil levels do not produce any meaningful impact on the simulation compared to the original one. In any case, RAMS is able to properly capture the observed patterns in those cases where a Western advection is widely extended over the area of study. This region's variability in orography and in distances to the sea promotes the development of sea-breeze circulations, thus producing a convergence of two opposite wind flows, a Western synoptic advection and a sea-breeze circulation. As a result, the RAMS skill in those cases where a sea breeze is well developed depends on the proper location of the boundary and convergence lines of these two flows. ; This work has been funded by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (FEDER) through the project CGL2015-64268-R (MINECO/FEDER, UE), by the Regional Government of Valencia through the project PROMETEOII/2014/086 and by the Spanish Ministerio de Economía y Competitividad through the project CGL2011-30433-C02-02 (MINECO).

Abstract. This article investigates the potential of W-band radar reflectivity to
improve the quality of analyses and forecasts of heavy precipitation events
in the Mediterranean area. The "1D+3DVar" assimilation method,
operationally employed to assimilate ground-based precipitation radar data in
the Météo-France kilometre-scale numerical weather prediction (NWP)
model AROME, has been adapted to assimilate the W-band reflectivity measured
by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere)
during a 2-month period over the Mediterranean area. After applying a bias
correction, vertical profiles of relative humidity are first derived via a
1-D Bayesian retrieval, and
then used as relative humidity pseudo-observations in the 3DVar assimilation
system of AROME. The efficiency of the 1-D Bayesian method in retrieving
humidity fields is assessed using independent in-flight humidity
measurements. To complement this study, the benefit brought by consistent
thermodynamic and dynamic cloud conditions has been investigated by
separately and jointly assimilating the W-band reflectivity and horizontal
wind measurements collected by RASTA in the 3 h 3DVar assimilation system of
AROME. The data assimilation experiments are conducted for a single heavy precipitation event
and then also for 32 cases. Results indicate that the W-band reflectivity has a larger
impact on the humidity, temperature and pressure fields in the analyses compared to the
assimilation of RASTA wind data alone. Besides, the analyses get closer to independent
humidity observations if the W-band reflectivity is assimilated alone or jointly with
RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more
rapidly as the forecast range increases when compared to the assimilation of RASTA wind
data alone. Generally, the joint assimilation of the W-band reflectivity with wind data
results in the best improvement in the rainfall precipitation forecasts. Consequently,
results of this study indicate that consistent thermodynamic and dynamic cloud conditions
in the analysis leads to an improvement of both model initial conditions and forecasts.
Even though to a lesser extent, the assimilation of the W-band reflectivity alone also
results in a slight improvement of the rainfall precipitation forecasts.

Despite their potential as a naturally-available clean energy option, the renewable energy resources of the Democratic People's Republic of Korea (i.e., North Korea) have rarely been evaluated. Therefore, to estimate the availability of land surface solar irradiance necessary for solar applications and to model available energy potential, physically-based models drawing on Communication, Ocean and Meteorological Satellite (COMS) data and associated statistics for key atmospheric constituents, were employed. To assess wind energy resources, model output statistics (MOS) were integrated from post-processed Local Data Assimilation and Prediction System (LDAPS) variables, thereby removing any systematic bias arising from long-term regression methods. The root mean square error (RMSE) and mean bias error (MBE) served to compare pyranometer- and satellite-sourced solar radiation, under instantaneous (87.90 W m−2 and 16.84 W m−2, respectively) and daily 'all sky conditions' (624.98 Wh m−2 d−1 and 13.89 Wh m−2 d−1, respectively). These low values indicate that satellite-based solar irradiance is sufficiently accurate to be used to model future land surface solar energy in North Korea. In the evaluation of wind energy resources, daily wind speeds obtained from Numerical Weather Prediction (NWP) reanalysis fields showed good accuracy compared to a meteorological tower measurement (RMSE = 0.37 m s−1 and MBE = 0.24 m s−1). In the study region, mean wind energy potential (from 2013–2015) was 3.44 kWh m−2 d−1, whereas solar energy potential was slightly lower at 3.36 kWh m−2 d−1; this can be attributed to the nation's mountainous terrain and high latitude. Although the region's mountainous terrain may be an obstacle for future development of renewable energy infrastructure, these initial annual mean solar and wind power density results illustrate the significant renewable energy potential of North Korea. This situates the country in a position to promote the United Nations Sustainable Development Goal (SDG #7) of integrating cleaner and more sustainable energy resources through solar and wind power.

[Abstract] The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a Numerical Weather Prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An Artificial Neural Network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available. ; Universidade de Vigo; 00VI 131H 641.02 ; This investigation article was partially supported by the University of Vigo through the grant Convocatoria de Axudas á Investigación 2018: Axudas Predoutorais UVigo 2018 (grant number 00VI 131H 641.02). This investigation article was also partially supported by the Ministry of Universities of the Spanish Government through the grant Ayudas para la Formación de Profesorado Universitario: Convocatoria 2017 (grant number FPU17/01834). This investigation article was also partially supported by the Ministry of Universities of the Spanish Government by means of the SMARTHERM (Project: RTI2018-096296-B-C2)

Abstract. The case of the heavy precipitation event on 14 and 15 October 2018 which has led to severe flash flooding in the Aude watershed in south-western France is studied from a meteorological point of view using deterministic and probabilistic numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. This case features typical characteristics of Mediterranean heavy precipitation events such as its classic synoptic situation and its quasi-stationary convective precipitation that regenerates continuously, as well as some peculiarities such as the presence of a former hurricane and a pre-existing cold air mass close to the ground. Mediterranean Sea surface temperature and soil moisture anomalies are briefly reviewed, as they are known to play a role in this type of hydrometeorological events.
A study of rainfall forecasts shows that the event had limited predictability, in particular given the small size of the watersheds involved.
It is shown that the stationarity of precipitation, whose estimation benefits from data from personal stations, is linked to the presence near the ground of a trough and a strong potential virtual temperature gradient, the stationarity of both of which is highlighted by a combination of observations from standard and personal stations. The forecast that comes closest to the rainfall observations contains the warmest, wettest, and fastest low-level jet and also simulates near the ground a trough and a marked boundary between cold air in the west and warm air in the east, both of which are stationary.