Meteorological Situations Research Articles

Methods of studying and forecasting of storm winds in the territory of Russia

This review paper provides information about methods of storm wind research and forecasting. A storm wind is a dangerous hydrometeorological phenomenon that occurs on land or causes strong waves at sea. The mechanism of development of this phenomenon is associated with a powerful action of some conventional processes in the atmosphere. The importance of studying and forecasting storm-force winds is related to the damage that such winds can cause. Currently, the observation network in Russia is still insufficient for a full-scale assessment of convective phenomena. An exception is the Central Federal District, where there is a continuous coverage of the territory by observation data. Therefore, an important step in this direction is to equip the country’s territories with a network of sensors for continuous recording of the meteorological situation. The most optimal device that allows determining the structure and physical characteristics of the origins of storm winds is the weather radar. Another comprehensive approach to assessing the origins and passage of storm winds is the use of remote sensing data. Methods based on the non-hydrostatic general use models WRF-NMM (Weather Research and Forecasting-Nonhydrostatic Mesoscale Model) and WRF-ARW (Weather Research and Forecasting) have made significant progress in predicting storm winds. These models have been developed in the USA. The world practice of forecasts shows that these models can reproduce the mesoscale convective processes quite well and be used in a system of early warning of the danger of strong winds.

Impacts of urbanization and long-term meteorological variations on global PM2.5 and its associated health burden.

PM2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM2.5 concentration globally. The PM2.5-caused mortality density (deaths/100km2) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM2.5-caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM2.5-caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270600 (196800-317900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures.

The 2018 northern European hydrological drought and its drivers in a historical perspective

Abstract. In 2018, large parts of northern Europe were affected by an extreme drought. A better understanding of the characteristics and the large-scale atmospheric circulation driving such events is of high importance to enhance drought forecasting and mitigation. This paper examines the historical extremeness of the May–August 2018 meteorological situation and the accompanying meteorological and hydrological (streamflow and groundwater) drought. Further, it investigates the relation between the large-scale atmospheric circulation and summer streamflow in the Nordic region. In May and July 2018, record-breaking temperatures were observed in large parts of northern Europe associated with blocking systems centred over Fennoscandia and sea surface temperature anomalies of more than 3 ∘C in the Baltic Sea. Extreme meteorological drought, as indicated by the 3-month Standardized Precipitation Index (SPI3) and Standardized Precipitation Evapotranspiration Index (SPEI3), was observed in May and covered large parts of northern Europe by July. Streamflow drought in the Nordic region started to develop in June, and in July 68 % of the stations had record-low or near-record-low streamflow. Extreme streamflow conditions persisted in the southeastern part of the region throughout 2018. Many groundwater wells had record-low or near-record-low levels in July and August. However, extremeness in groundwater levels and (to a lesser degree) streamflow showed a diverse spatial pattern. This points to the role of local terrestrial processes in controlling the hydrological response to meteorological conditions. Composite analysis of low summer streamflow and 500 mbar geopotential height anomalies revealed two distinct patterns of summer streamflow variability: one in western and northern Norway and one in the rest of the region. Low summer streamflow in western and northern Norway was related to high-pressure systems centred over the Norwegian Sea. In the rest of the Nordic region, low summer streamflow was associated with a high-pressure system over the North Sea and a low-pressure system over Greenland and Russia, resembling the pattern of 2018. This study provides new insight into hydrometeorological aspects of the 2018 northern European drought and identifies large-scale atmospheric circulation patterns associated with summer streamflow drought in the Nordic region.

Establishment of Conceptual Schemas of Surface Synoptic Meteorological Situations Affecting Fine Particulate Pollution Across Eastern China in the Winter

AbstractIn the present study, the characteristics of weather conditions and local meteorological variables over the Beijing‐Tianjin‐Hebei (BTH) and the Yangtze River Delta (YRD) regions in the winter are analyzed using the principal component analysis (PCA) method and daily PM2.5 accumulation rate. Typical synoptic weather patterns over China in the winter can be classified into four types. During the Type 1 synoptic weather pattern, China is under the influence of the Siberian High, and northerly winds prevail; this situation is beneficial to the transport of pollutants from north to south. However, when the Siberian High is weak, southerly winds prevail which may result in the transport of pollutants from south to north. The Type 2 weather pattern refers to a weak high pressure located in the BTH resulting in the accumulation of pollutants. During the Type 3 weather pattern, an intense cold Siberian High moves to the south and affects the northern areas of China. The associated front brings heavy precipitation in the YRD resulting in the wet deposition of pollution. During the Type 4 weather condition, the weak Siberian High is blocked by the northeast cold vortex and moves towards the south, causing the accumulation of pollution in the YRD. The PCA model shows that there are two transport pathways for pollutants to the BTH (YRD) area: one from the YRD (BTH) and Shandong during Types 1 and 2 (Types 1 and 3) situations and the other one from the central provinces during Type 4 (Types 1 and 4).

Wind direction retrieval from Sentinel-1 SAR images using ResNet

This paper introduces a novel approach to estimate the wind direction over the sea from Synthetic Aperture Radar (SAR) images without any external information. The method employs deep residual network (ResNet), a variant of Convolutional Neural Network, to obtain high resolution (2 km by 2 km) aliased wind direction fields. Forty-seven SAR images of the European Space Agency satellites Sentinel-1 have been processed with ResNet, previously trained with other fifteen images. The areas of interest are the Mediterranean Sea and the Persian Gulf, two regional seas where the SAR images often present complex patterns associated to the wind field spatial structure reporting traces of the interaction with coastal orography, hence valuable test sites to evaluate the performance of the methodology here proposed. Statistical analysis was carried out comparing the SAR-derived wind directions with those from ECMWF atmospheric model, ASCAT scatterometer and in-situ gauges. It reports biases β of -1.1°, 2.4° and -4.6° respectively, and centered root mean square difference cRMSd<21°, consistent with the benchmark obtained comparing scatterometer with ECMWF wind directions over the areas imaged by SAR (β 2.1°, cRMSd=19°). These results are relevant because they include the coastal data not accounted in the benchmark. Analysis of selected cases showed that SAR-derived wind fields reproduce meteorological situations characterized by strong divergence. Notably, our ResNet is able to estimate the wind direction even in the absence of wind streaks on the SAR images and in presence of convective turbulence structures, atmospheric lee waves, and ships. Furthermore, the model is also able to derive the wind field over small areas, as the example of Venice lagoon has shown. Detailed analysis of selected cases raised the issue of the lack of data with true spatial resolution of ≈2 km and within half hour from the satellite pass time necessary for exhaustive comparisons.

Unusual appearance of mother‐of‐pearl clouds above El Calafate, Argentina (50°21′S, 72°16′W)

Visual observations from the ground and from a glider soaring in the lowermost stratosphere revealed the existence of stratospheric mother-of-pearl clouds above El Calafate in the lee of the Andes on 11 September 2019. The appearance of these clouds is rather unusual considering the time – end of the austral winter – and the location at about 50°S, being far away from Antarctica. This paper presents the available observations and describes the overall meteorological situation that was related to the earliest sudden stratospheric warming recorded so far in the Southern Hemisphere. By using high-resolution numerical simulations, we show evidence of mountain waves propagating up to the stratosphere that are responsible for generating the localised cold stratospheric temperature anomalies required for ice cloud formation. Snapshots of a mother-of-pearl cloud from the camera installed at the PERLAN 2 aircraft's tail wing.

Estimating optimal localization for sampled background‐error covariances of hydrometeor variables

AbstractKilometre‐scale numerical weather prediction addresses the challenge of forecasting accurately clouds and precipitation. Ensemble‐based data assimilation methods make use of background‐error covariances that are sampled from an ensemble of forecasts. These methods can be considered in order to include hydrometeor variables and their flow‐dependent error covariances in the data assimilation system. Yet, because of limited ensemble size, rank deficiency of the resulting covariances and sampling noise occur, which can be mitigated by a localization procedure. In order to optimally localize covariances for hydrometeor variables, previous work by the authors has been extended. This approach estimates localization as a linear filtering on covariances, optimal in the sense of minimizing sampling noise. The zero‐variance and the high spatial variability issues met with hydrometeor variables are addressed by using an improved method for spatial sampling, based on geographical masks. Diagnosed optimal horizontal localization lengths appear to be much shorter for hydrometeors than for other classical thermodynamic variables. Conversely, we report optimal vertical localization to be very broad for precipitating species. Great variability between different meteorological situations has also been noticed, which reflects the high flow dependency of hydrometeor forecast errors. This suggests that ensemble‐based data assimilation schemes that consider hydrometeors as control variables should adopt more refined localization schemes than the common “one‐size‐fits‐all” approach.

The significance of extreme floods in the transformation of mountain valleys and causing flood risk, on the example of the July 2001 flood that occurred in the upper catchment of the river Skawa

In July 2001, in the Carpathian basin of the Vistula, there was a lot of rainfall and storms. The meteorological situation of that time was similar to that of 1934 when a great flood occurred. On 25 July (2001st), in the upper part of the Skawa catchment, a violent storm occurred. Its centre was located right at Makowska Góra. The daily precipitation in Maków Podhalański was 190.8 mm that day. Most of the precipitation occurred during a storm. Although the precipitation was much lower in the other stations located in the drainage basin, the flow of the Skawa in Sucha Beskidzka was 660 m3 s-1, while the constructed dam in Świnna-Poręba – 1019 m3 s-1. Precipitation was so abundant that the floodplains terraces of the Skawa have been inundated, and made the streams flowing down the Makowska Mountain spill out of the trough. The centre of Maków Podhalański and the neighbouring streets were destroyed. The main current flowed through the streets of Źródlana, Krótka, Kościelna, Rynek, and Wolności. The biggest losses were caused by the Księży Potok and several smaller streams (Rzyczki, Grabce, and Czarny Potok) that poured out of the trough and flowed through them. The biggest losses have been incurred by the Budzów and Zembrzyce municipalities located on the other side of the mountain. The losses were caused by a small Paleczka stream.

A GIS plugin to model the near surface air temperature from urban meteorological networks

This article proposes a QGIS plug-in called TempMap, to obtain near surface air temperature maps from an urban meteorological network, the topography description and three parameters describing the urbanized surface fraction. The objective being to facilitate the communication and consideration of local climate in French urban planning practices, some methodological choices were done related to the meteorological situation robustness, the choice of the mapped indicator and the availability of the surface data description. The plug-in allows to apply spatial analytical modelling based on a hybrid interpolation and is here applied to the CAPITOUL atmospheric database of Toulouse (France), the MApUCE urban database and the topographical information from the French National Geographical Institute to obtain air temperature maps for four Local Weather Types. The plug-in can be download from GitHub, is compatible with all QGIS versions since 2.14.12. The evaluation of the model results shows an accuracy comparable to the state-of-the-art with a maximum delta between predicted and observed values of ~1 °C. Here French datasets are used but alternative datasets can be used being also possible to modify the output resolution in order to fit the input data resolution and the needs of the study.

The gust that never was: a meteorological instrumentation mystery

During the early hours of Easter Monday, 28 March 2016, the passage of a very rapidly-deepening depression across central England resulted in a rare inland gale, followed by a succession of squally showers during the afternoon in the polar maritime air behind the cold front. During one of these showers, two anemometers (at 2 m and 10 m above ground level) located in the Atmospheric Observatory at the University of Reading recorded gusts exceeding 30 m/s (60 kn), the highest recorded at the site for almost 20 years. Although these were well in excess of other local gust speeds, the meteorological situation was favourable for very high squally gusts, and this taken together with records from a well-maintained site with impeccable site and instrument metadata initially suggested the observations were beyond reproach. However, it soon became clear that wind speeds from other anemometers on the site (one on the same mast) did not support these extreme gusts. A careful investigation into the circumstances concluded that both anemometer outputs were temporarily incremented by rapid pulses resulting from coronal discharge (St Elmo’s fire), even though observed electric fields were not sufficiently large to result in lightning. Other instances of observed high wind gusts from similar instruments, apparently genuinely recorded but lacking supportive evidence in terms of substantive infrastructure damage or confirmation from a second instrument, may now warrant retrospective re-examination.

Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement

Abstract. The intensity and phase of precipitation at the ground surface can have important implications not only for meteorological and hydrological situations but also in terms of hazards and risks. In the field, Thies disdrometers are sometimes used to monitor the quantity and nature of precipitation with high temporal resolution and very low maintenance and thus provide valuable information for the management of meteorological and hydrological risks. Here, we evaluate the Thies disdrometer with respect to precipitation detection, as well as the estimation of precipitation intensity and phase at a pre-alpine site in Switzerland (1060 m a.s.l.), using a weighing precipitation gauge (OTT pluviometer) and a two-dimensional video disdrometer (2DVD) as a reference. We show that the Thies disdrometer is well suited to detect even light precipitation, reaching a hit rate of around 95 %. However, the instrument tends to systematically underestimate rainfall intensities by 16.5 %, which can be related to a systematic underestimation of the number of raindrops with diameters between 0.5 and 3.5 mm. During snowfall episodes, a similar underestimation is observed in the particle size distribution (PSD), which is, however, not reflected in intensity estimates, probably due to a compensation by snow density assumptions. To improve intensity estimates, we test PSD adjustments (to the 2DVD) and direct adjustments of the resulting intensity estimates (to the OTT pluviometer), the latter of which are able to successfully reduce the systematic deviations during rainfall in the validation period. For snowfall, the combination of the 2DVD and the OTT pluviometer seems promising as it allows for improvement of snow density estimates, which poses a challenge to all optical precipitation measurements. Finally, we show that the Thies disdrometer and the 2DVD agree well insofar as the distinction between rain and snowfall is concerned, such that an important prerequisite for the proposed correction methods is fulfilled. Uncertainties mainly persist during mixed-phase precipitation or low precipitation intensities, where the assignment of precipitation phase is technically challenging, but less relevant for practical applications. We conclude that the Thies disdrometer is suitable not only to estimate precipitation intensity but also to distinguish between rain and snowfall. The Thies disdrometer therefore seems promising for the improvement of precipitation monitoring and the nowcasting of discharge in pre-alpine areas, where considerable uncertainties with respect to these quantities are still posing a challenge to decision-making.

An accurate thermal model for the PV electric generation prediction: long-term validation in different climatic conditions

Solar radiation incident on photovoltaic modules only partly directly convert into electricity; the rest is converted into heat that increases the module layers’ temperature. In order to quantify both the output power and the electrical efficiency, the knowledge of the temperature profile is essential. This study proposes a transient one-dimensional thermal model of photovoltaic modules which provides the temperature distribution across the panel thickness, used to predict the electricity production under variable operating weather conditions. The model was implemented and validated considering the module back surface temperature and the produced electric power measured in an experimental set-up located at the University of Calabria (Italy). A more detailed evaluation of the long-wave radiative heat exchange between the front glass cover and the external environment is considered, employing experimental sky temperatures data. Different formulations of the heat transfer coefficient were tested to provide more accurate results. To show the reliability of the model predictions over a wide range of operating conditions, the validation was conducted considering several days of each season with different meteorological situations. The accuracy of the model was proved by statistical parameters showing the excellent agreement between the predicted and measured temperatures and power outputs.

Wildfires in Northern Portugal: an operational case study in Tabuaço

The present work is an analysis of the wildfire that occurred in Tabuaço (Portugal). This wildfire developed in a complex topography and under extreme meteorological conditions. The wind was one of the most difficult factors because it increased the rate of spread of the fire front and increased the number of spot fires in front of the head fire. This kind of situation is very often a source of incidents or accidents. Additionally, the fire progressed in zones where the wildland–urban interface was practically non-existent, but also in the areas where the existence of settlements gave rise to critical points, related to the necessity to protect people and property. The suppression manoeuvres of the fire of Tabuaço were carried out in a way that led to a high level of safety, without victims or serious injuries recorded, and so they can be considered an example of a wildfire fighting approach. The use of fire to fight a fire is an important technique when it is well applied. An analysis of the existing conditions, such as geographic characteristics, meteorological situation, topography, fuels and infrastructures of defence against forest fires and fire development and behaviour, are described and analysed in a detailed way.

A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data

Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage.

Are Empirical Equations an Appropriate Tool to Assess Separation Distances to Avoid Odour Annoyance?

Annoyance due to environmental odour exposure is in many jurisdictions evaluated by a yes/no decision. Such a binary decision has been typically achieved via odour impact criteria (OIC) and, when applicable, the resultant separation distances between emission sources and residential areas. If the receptors lie inside the required separation distance, odour exposure is characterised with the potential of causing excessive annoyance. The state-of-the-art methodology to determine separation distances is based on two general steps: (i) calculation of the odour exposure (time series of ambient odour concentrations) using dispersion models and (ii) determination of separation distances through the evaluation of this odour exposure by OIC. Regarding meteorological input data, dispersion models need standard meteorological observations and/or atmospheric stability typically on an hourly basis, which requires expertise in this field. In the planning phase, and as a screening tool, an educated guess of the necessary separation distances to avoid annoyance is in some cases sufficient. Therefore, empirical equations (EQs) are in use to substitute the more time-consuming and costly application of dispersion models. Because the separation distance shape often resembles the wind distribution of a site, wind data should be included in such approaches. Otherwise, the resultant separation distance shape is simply given by a circle around the emission source. Here, an outline of selected empirical equations is given, and it is shown that only a few of them properly reflect the meteorological situation of a site. Furthermore, for three case studies, separation distances as calculated from empirical equations were compared against those from Gaussian plume and Lagrangian particle dispersion models. Overall, our results suggest that some empirical equations reach their limitation in the sense that they are not successful in capturing the inherent complexity of dispersion models. However, empirical equations, developed for Germany and Austria, have the potential to deliver reasonable results, especially if used within the conditions for which they were designed. The main advantage of empirical equations lies in the simplification of the meteorological input data and their use in a fast and straightforward approach.

Spatio-temporal discrimination of molecular, aerosol and cloud scattering and polarization using a combination of a Raman lidar, Doppler cloud radar and microwave radiometer.

The combined data from the ESA Mobile Raman Polarization and Water Vapor Lidar (EMORAL), the LATMOS Bistatic Doppler Cloud Radar System for Atmospheric Studies (BASTA), and the INOE Microwave Radiometer (HATPRO-G2) have been used to explore the synergy for the spatio-temporal discrimination of polarization and molecular, aerosol and cloud scattering. The threshold-based methodology is proposed to perform an aerosol-cloud typing using the three instruments. It is demonstrated for 24 hours of observations on 10 June 2019 in Rzecin, Poland. A new scheme for target classification, developed collaboratively by the FUW and the OUC, can help determine molecules, aerosol (spherical, non-spherical, fine, coarse), cloud phase (liquid, ice, supercooled droplets) and precipitation (drizzle, rain). For molecular, aerosol, and cloud discrimination, the thresholds are set on the backward scattering ratio, the linear particle depolarization ratio and the backscatter colour ratio, all calculated from lidar signals. For the cloud phase and precipitation categorization, the thresholds are set on the reflectivity and the Doppler velocity derived from cloud radar signals. For boundary layer particles, precipitation, and supercooled droplets separation, the thresholds are set on the profiles of temperature and relative humidity obtained by the microwave radiometer. The algorithm is able to perform separation even under complicated meteorological situation, as in the presented case study.

Evaluation of the AquaCrop model for winter wheat under different irrigation optimization strategies at the downstream Kabul River Basin of Afghanistan

Afghanistan has an arid to semi-arid climate where irrigated agriculture largely depends on scarce irrigation water supplies from snowmelt from the high raised mountains. Under growing water scarcity, farmers not only need to use the available water more wisely but have to develop alternative options for coping water scarcity. Deficit irrigation schedule can be one of the options to mitigate the adverse impacts of water scarcity on crop production. In the current study, FAO’s crop water productivity model (AquaCrop) was calibrated and validated with field data in Kabul River Basin (KRB) for wheat crop to simulate four different water scarcity scenarios (S-A: business-as-usual scenario, S-B: refilling the soil profile to field capacity upon 50 % water depletion, S-C: refilling the soil profile upon 100 % depletion and S-D: refilling the soil profile upon 130 % depletion occurrence) for resultant yield, water productivity (WP) and biomass production. Two wheat fields, namely A and B were monitored intensively for soil moisture content, meteorological situation, irrigation application and post-harvest data. Results show that the measured WP was 1.4 kg m−3 ETa and 1.5 kg m−3 ETa whereas, the actual (measured) water use efficiency (WUE) was 0.58 kg m−3 and 0.66 kg m−3 for Field A and Field B, respectively. The WP of the scenarios S-A, S-B, S-C and S-D was 2.0-2.1 kg m−3 ETa (for plot B and A), 2.5 kg m−3 ETa, 2.74 kg m−3 ETa and 2.8 kg m−3 ETa respectively. Similarly, yield under these scenarios was 6.4 ton ha−1, 8.7 ton ha−1, 7.4 ton ha−1and 6.7 ton ha−1 respectively while the above ground biomass was 21.3 ton ha−1, 21.8 ton ha−1, 19 ton ha−1 and 18.3 ton ha−1 respectively. As a consequence, WP could increase by 92.8 %, 78 % and 95 % in S-B, S-C and S-D, respectively with reference to the measured WP. The optimized scenarios developed in this study can provide guidelines for policy makers and farming communities to mitigate the adverse impact of water scarcity through such innovative interventions.

High Ambrosia pollen concentrations in Poland respecting the long distance transport (LDT)

High Ambrosia pollen concentrations in Poland rather rarely come from the local sources. The aim of this study was to define the temporal and spatial differences of the high Ambrosia pollen concentrations by creating models for the pollen transport from the distant sources. This study was thought to determine the direction of the air masses inflow into Poland, carrying Ambrosia pollen, from areas of the bordering countries with the pollen concentrations higher than iSTOTEN_n Poland. Pollen and meteorological datasets at 8 monitoring sites in Poland, and daily pollen concentrations at 11 sites in the Czech Republic, 5 sites in Slovakia and 3 sites in Ukraine were analysed recently. Days with concentrations ≥10 Pollen/m3 and concurrent meteorological situations were analysed in great deal. The HYSPLIT model was applied to compute backward trajectories up to 4 days backward (96 h) and at three altitudes: 20, 500 and 1000 m above ground level (a.g.l.). High pollen concentrations occur most frequently when the air masses inflow into Poland from southerly (S, SE, SW, 44%) and easterly (E, 6%) directions and in no advection situations (25%). In years with the highest frequency of days over 10 Pollen/m3, the prevailing directions of the pollen influx into Poland were from the South (2004–2006, 2008, 2011) but in one year (2014) from the East. Trajectories for the studied period show that air masses come most frequently from Slovakia and the Czech Republic. Sometimes, the Ambrosia pollen transport happens from Ukraine.

Assessing the Skill and Reliability of Seasonal Climate Forecasts in Sahelian West Africa

Abstract Seasonal climate forecasts have the potential to support planning decisions and provide advanced warning to government, industry, and communities to help reduce the impacts of adverse climatic conditions. Assessing the reliability of seasonal forecasts, generated using different models and methods, is essential to ensure their appropriate interpretation and use. Here we assess the reliability of forecasts for seasonal total precipitation in Sahelian West Africa, a region of high year-to-year climate variability. Through digitizing forecasts issued from the regional climate outlook forum in West Africa known as Prévisions Climatiques Saisonnières en Afrique Soudano-Sahélienne (PRESASS), we assess their reliability by comparing them to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) project observational data over the past 20 years. The PRESASS forecasts show positive skill and reliability, but a bias toward lower forecast probabilities in the below-normal precipitation category. In addition, we assess the reliability of seasonal precipitation forecasts for the same region using available global dynamical forecast models. We find all models have positive skill and reliability, but this varies geographically. On average, NCEP’s CFS and ECMWF’s SEAS5 systems show greater skill and reliability than the Met Office’s GloSea5, and in turn than Météo-France’s Sys5, but one key caveat is that model performance might depend on the meteorological situation. We discuss the potential for improving use of dynamical model forecasts in the regional climate outlook forums, to improve the reliability of seasonal forecasts in the region and the objectivity of the seasonal forecasting process used in the PRESASS regional climate outlook forum.

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Abstract. Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.