Automatic Weather Stations Research Articles

Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica

Abstract. The datasets presented in this article were collected during a 4-month measurement campaign at the Belgian research base Princess Elisabeth Antarctica (PEA). The campaign, named PEA Orographic Precipitation Experiment (POPE), was conducted by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne, with the logistical support of the International Polar Foundation, between the end of November 2019 and the beginning of February 2020. The datasets have been collected at five different sites. A W-band Doppler cloud profiler and a multi-angle snowflake camera (MASC) have been deployed in the immediate proximity of the main building of the station. An X-band dual-polarization Doppler scanning weather radar was installed 1.9 km southeast of PEA. Information on the various hydrometeor types have been derived from its measurements and from the images collected by the MASC. The remaining three sites were located in a transect across the mountain chain south of the base, between 7 and 17 km apart from each other. At each site, a K-band Doppler profiler and an automated weather station have been deployed. A pyrgeometer and a pyranometer accompanied the instruments at the site in the middle of the transect. The radar variables and the measurements collected by the weather stations and radiometers are available at https://doi.org/10.5281/zenodo.7428690 (Ferrone and Berne, 2023c). The Doppler spectra are available at https://doi.org/10.5281/zenodo.7507087 (Ferrone and Berne, 2023a) and https://doi.org/10.5075/epfl-lte-299685 (Ferrone and Berne, 2023b). A case study, covering the precipitation event recorded on 23 December 2019, is presented to illustrate the various datasets. Overall, the availability of radar measurements over a complex terrain, relatively far from a scientific base, is extremely rare in the Antarctic context and opens a wide range of possibilities for precipitation studies over the region.

Correction of precipitation measurement for weighing precipitation gauges in a glacierized basin in the Tianshan Mountains

Precipitation is one of the most important climatological data for global hydrothermal cycle and climate change. The accuracy of precipitation data not only directly affects the hydrological processes, but also plays an important role in the climate and hydrology at regional and global scales. According to the in situ datasets, the precipitation measurement in automatic weather stations for Geonor T-200B was corrected by the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE) transfer functions. The parameters of transfer functions were tested and recalibrated by the local datasets. The results showed that the transfer functions showed better performance after recalibrating parameters by the local datasets. The root-mean-square error (RMSE) and mean bias decreased by an average of 34% and 42%, respectively. The corrected snowfall increased by 7% (14 mm) at the test station. Then, the new parameters were used in other automatic weather stations to correct precipitation, and it was found that solid precipitation was underestimated by 13% on the glacier surface affected by wind speed. Moreover, according to the corrected precipitation datasets observed in automatic weather stations and national meteorological stations, the precipitation–altitude relationship in the Urumqi River Basin was analyzed. The annual precipitation gradient was 115 mm km-1, and the maximum seasonal altitude occurred in summer with a value of 35 mm km-1 and in autumn with the lowest value of 1 mm km-1. When considering precipitation on the glacier surface, the yearly precipitation gradient was increased with the value of 158 mm km -1 in 2019.

Long time series (1984–2020) of albedo variations on the Greenland ice sheet from harmonized Landsat and Sentinel 2 imagery

AbstractAlbedo is a key factor in modulating the absorption of solar radiation on ice surfaces. Satellite measurements have shown a general reduction in albedo across the Greenland ice sheet over the past few decades, particularly along the western margin of the ice sheet, a region known as the Dark Zone (albedo < 0.45). Here we chose a combination of Landsat 4–8 and Sentinel 2 imagery to enable us to derive the longest record of albedo variations in the Dark Zone, running from 1984 to 2020. We developed a simple, pragmatic and efficient sensor transformation to provide a long time series of consistent, harmonized satellite imagery. Narrow to broadband conversion algorithms were developed from regression models of harmonized satellite data and in situ albedo from the Program for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather stations. The albedo derived from the harmonized Landsat and Sentinel 2 data shows that the maximum extent of the Dark Zone expanded rapidly between 2005 and 2007, increasing to ~280% of the average annual maximum extent of 2900 km2 to ~8000 km2 since. The Dark Zone is continuing to darken slowly, with the average annual minimum albedo decreasing at a rate of $\sim \!-0.0006 \pm 0.0004 \, {\rm a}^{-1}$ (p = 0.16, 2001–2020).

Analysis and characterisation of extreme wind gust hazards in New South Wales, Australia

Extreme wind gusts cause major socioeconomic damage, and the rarity and localised nature of those events make their analysis challenging by either modelling or empirical approaches. A 23-year long data record from 29 automatic weather stations located in New South Wales (eastern Australia) is used to study the distribution, frequency and average recurrence intervals (ARIs) of extreme gusts via a peaks-over-threshold approach. We distinguish between gust events generated by synoptic phenomena (e.g. cyclones and frontal systems), hereafter called “synoptic events”, and convective phenomena (i.e. thunderstorms), hereafter called “convective events”, using the wind time series. For synoptic events the frequency of gusts >25 m/s decreases systematically inland from the coast, in contrast to convective gusts which are more uniformly distributed geographically and occur more often than synoptic gusts at nearly all inland locations. At inland locations the most extreme wind gusts are likewise dominated by convective events, whereas at coastal stations both gust types have similar intensities at low ARIs but convective events again dominate at the highest ARIs. Extreme gust directions were found to be predominantly westerly at inland locations and southerly at coastal ones, with more variable direction for convective than synoptic events. This study confirms the dominant role of thunderstorms in producing the most extreme gusts in the region, and shows that wind risk varies strongly with distance from the coast.

Influence of wind speed on rain-based attenuation at Ku-band for earth-satellite links in Nigeria

The peculiarity of the tropical climate shows that when the quality of service (QoS) on Satellite-Earth propagation links is to be determined, the statistics of rain-based attenuation (RbA), storm speed, and their impacts are the important parameters to be considered, especially at frequencies above 10 GHz. This paper assesses the influence of storm speed in estimating RbA at Ku-band, based on 2-year rain rate data obtained using automatic weather station (AWS) and RbA beacon measurements in Nigeria. The rain rates based on the time series were employed to deduce the time series RbA based on the synthetic storm technique (SST) algorithm. The results show a seasonal pattern of rain rate that correlates with the SST-based RbA. The RbA generated closely follows suit with the beacon measurement, especially under low wind speed, and outperforms the international telecommunications union–radiocommunication sector (ITU-R) model based on the lowest metric measures. However, at a higher storm speed, the RbA generated deviated widely from the measured RbA values by about 16%. These results are crucial for figuring out what needs to be done to protect QoS in a tropical area where wind and rain are common.

NIGHTTIME LIGHT: A POTENTIAL PROXY FOR LOCAL NOCTURNAL URBAN HEAT ISLAND INTENSITY IN SEOUL

ABSTRACT Nighttime light (NTL) imagery provides valuable information for monitoring urban development, human activity, and, consequently, urban thermal environments. However, only a few studies have attempted to directly draw linkages between NTL and urban heat island (UHI) formation. Establishing associations between NTL and UHI indicators could potentially ease the process of identifying heightened thermal environments and thus pinpointing urban hotspots in need of policy interventions and strategic mitigation measures. In the present study, through clustering and correlation analysis, we study ties between NTL, surface urban heat island intensity (SUHII) and atmospheric urban heat island intensity (AUHII) in Seoul. NTL data was derived from the Suomi National Polar-Orbiting Partnership-Visible and Infrared imager/Radiometer Suite (SNPP-VIIRS), while SUHII data was obtained from the moderate resolution imaging spectroradiometer (MODIS) satellite instrument. Additionally, AUHII was computed from archived weather data collected from multiple automatic weather stations (AWSs) in Seoul. Our results show that NTL is highly positively correlated with SUHII (R > 0.86, p < 0.05) and AUHII (R > 0.68, p < 0.05). The findings highlight the potential usage of NTL as a proxy for UHI formation, simplifying the process of identifying heightened thermal areas in Seoul that may require rapid policy intervention.

Climatology and surface impacts of atmospheric rivers on West Antarctica

Abstract. Atmospheric rivers (ARs) transport large amounts of moisture from the mid- to high-latitudes and they are a primary driver of the most extreme snowfall events, along with surface melting, in Antarctica. In this study, we characterize the climatology and surface impacts of ARs on West Antarctica, focusing on the Amundsen Sea Embayment and Marie Byrd Land. First, we develop a climatology of ARs in this region, using an Antarctic-specific AR detection tool combined with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) atmospheric reanalyses. We find that while ARs are infrequent (occurring 3 % of the time), they cause intense precipitation in short periods of time and account for 11 % of the annual surface accumulation. They are driven by the coupling of a blocking high over the Antarctic Peninsula with a low-pressure system known as the Amundsen Sea Low. Next, we use observations from automatic weather stations on Thwaites Eastern Ice Shelf with the firn model SNOWPACK and interferometric reflectometry (IR) to examine a case study of three ARs that made landfall in rapid succession from 2 to 8 February 2020, known as an AR family event. While accumulation dominates the surface impacts of the event on Thwaites Eastern Ice Shelf (> 100 kg m−2 or millimeters water equivalent), we find small amounts of surface melt as well (< 5 kg m−2). The results presented here enable us to quantify the past impacts of ARs on West Antarctica's surface mass balance (SMB) and characterize their interannual variability and trends, enabling a better assessment of future AR-driven changes in the SMB.

High-resolution 3D winds derived from a modified WISSDOM synthesis scheme using multiple Doppler lidars and observations

Abstract. The WISSDOM (Wind Synthesis System using Doppler Measurements) synthesis scheme was developed to derive high-resolution 3-dimensional (3D) winds under clear-air conditions. From this variational-based scheme, detailed wind information was obtained from scanning Doppler lidars, automatic weather stations (AWSs), sounding observations, and local reanalysis datasets (LDAPS, Local Data Assimilation and Prediction System), which were utilized as constraints to minimize the cost function. The objective of this study is to evaluate the performance and accuracy of derived 3D winds from this modified scheme. A strong wind event was selected to demonstrate its performance over complex terrain in Pyeongchang, South Korea. The size of the test domain is 12×12 km2 extended up to 3 km a.m.s.l. (above mean sea level) height with a remarkably high horizontal and vertical resolution of 50 m. The derived winds reveal that reasonable patterns were explored from a control run, as they have significant similarity with the sounding observations. The results of intercomparisons show that the correlation coefficients between derived horizontal winds and sounding observations are 0.97 and 0.87 for u- and v-component winds, respectively, and the averaged bias (root mean square deviation, RMSD) of horizontal winds is between −0.78 and 0.09 (1.77 and 1.65) m s−1. The correlation coefficients between WISSDOM-derived winds and lidar QVP (quasi-vertical profile) are 0.84 and 0.35 for u- and v-component winds, respectively, and the averaged bias (RMSD) of horizontal winds is between 2.83 and 2.26 (3.69 and 2.92) m s−1. The statistical errors also reveal a satisfying performance of the retrieved 3D winds; the median values of wind directions are −5 to 5 (0 to 2.5)∘, the wind speed is approximately −1 to 3 m s−1 (−1 to 0.5 m s−1), and the vertical velocity is −0.2 to 0.6 m s−1 compared with the lidar QVP (sounding observations). A series of sensitivity tests with different weighting coefficients, radius of influence (RI) in interpolation, and various combination of different datasets were also performed. The results indicate that the present setting of the control run is the optimal reference to WISSDOM synthesis in this event and will help verify the impacts against various scenarios and observational references in this area.

The Challenges of Micro-Nowcasting and the Women’s Slope Style Event at the PyeongChang 2018 Olympic Winter Games

The Women’s Slope Style event of 11–12 February 2018 at the PyeongChang 2018 Olympic Winter Games posed considerable challenges to the competitors and decision-makers, requiring sub-kilometer and sub-minute weather predictions in complex terrain. The gusty wind conditions were unfair and unsafe as the competitors could not achieve sufficient speed to initiate or complete their jumps. The term micro-nowcasting is used here to reflect the extreme high-resolution nature of these science and service requirements. The World Meteorological Organization has conducted several research development and forecast demonstration projects to advance, accelerate and promote the art of nowcasting. Data from compact automatic weather stations, located along the field of play, reported every minute and were post-processed using time series, Hovmöller and wavelet transforms to succinctly present the information. The analyses revealed dominant frequencies of about 20 min, presumed to be associated with vortex shedding from the mountain ridges, but were unable to directly capture the gusts that affected the competitors. The systemic challenges from this and previous projects are reviewed. They include the lack of adequate scientific knowledge of microscale processes, gaps in modeling, the need for post-processing, forecast techniques, managing ever-changing service requirements and highlights the role of observations and the critical role of the forecaster. These challenges also apply to future high-resolution operational weather and warning services.

Atmospheric Boundary Layer Over Ahmedabad, Western Indian Region: Impact of COVID-19 Nationwide Lockdown.

The government of India imposed a nationwide lockdown to tackle the outbreak of COVID-19 in 2020. This period witnessed record low anthropogenic activity, which had severe socio-economic impacts but also had orthogonal effects on the ambient air quality of the atmosphere. This study focuses on the variations in the atmospheric boundary layer (ABL) over a western Indian urban region in the light of COVID-19. Continuous backscatter recorded by a ceilometer, stationed at Ahmedabad, was used in this study to monitor the ABL during the national lockdown (NLD) in 2020 and state restrictions in 2021, and compared with the control year of 2019. In parallel, improvement in air quality during the NLD was observed by the SAFAR air quality station at Ahmedabad, with decreased particulate matter concentrations. The ground-based observations were substantiated by the ERA5 reanalysis dataset. A decline in the ABL height was recorded during the NLD, which showed improvement in 2021 but which was shy of the ABL in 2019. This was correlated with rain events during the observational period, recorded by an automatic weather station.

A Meteorological Analysis from the Southern Slope of Mt. Everest, Nepal

Mt. Everest is the highest mountain in the world, with an elevation ending at 8848.86 m above sea level, providing unique opportunity for direct observation of the upper troposphere. Utilizing the data from recently established five automatic weather stations (AWSs) network along the Everest climbing route, as part of the National Geographic and Rolex Perpetual Planet Expedition to Mount Everest 2019, from June 2019 to May 2020, this study investigates the meteorological environment over the southern slope of the Mt. Everest. Precipitation, temperature, radiations (income and outgoing short wave and long wave radiation), wind speed and direction along with derived variables like Lapse Rate, Precipitation Gradient, 6.11 hPa Isoline, and zero-degree Isotherm are analyzed with the aim of understanding altitudinal variation. Precipitation is mainly concentrated in monsoon with highest in Phortse (530 mm). Analysis of temperature lapse rate shows the highest lapse rate (-5.6 ℃ km-1) in monsoon and lowest in post-monsoon (-7℃ km-1). The precipitation analysis reveals that the vertical and horizontal precipitation gradient for monsoon is -63 mm km-1 and is -8.6 mm km-1 however, during the post-monsoon, precipitation increased by 0.75mm km-1 and 4.6 mm km-1, respectively. Similarly, westerly winds dominate during winter in upper station while it’s nearly uniform for lower stations. Radiation, likewise, are highly correlated between the stations, with incoming shortwave being the highest in the upper station, South-Col. Both isoline and isotherm lines are observed at around 6000 m above sea level. The one-year data has revealed some of the interesting pictures of high-altitude meteorology, but long-term data with fewer data gaps should be required to confirm these patterns.

The first Chinese automatic weather station on the Greenland ice sheet

The first Chinese automatic weather station on the Greenland ice sheet

The interactions between urban heat island and heat waves amplify urban warming in Guangzhou, China: Roles of urban ventilation and local climate zones

Under the background of global warming, interaction between heat waves (HWs) and urban heat island (UHI) has led to trends of increase in the intensity, frequency, and duration of extreme heat events in urban areas, seriously threatening the health of urban populations. Taking Guangzhou (a tropical megacity in China) as an example, this study used automatic weather station data and ERA5 reanalysis data to explore the interaction between HWs and UHI, and to elucidate the effects of wind speed and local climate zones (LCZs) on such interaction. Results revealed obvious HWs–UHI interaction in Guangzhou, whereby HWs induces an amplification effect on UHI intensity (UHII) that was most significant at night. In the main urban area, UHII and HWs both weakened with increasing wind speed, indicating that low wind speeds contribute to increased occurrence of HWs and enhancement of UHII. Differently, in some areas peripheral to the main urban area, the UHII at medium wind speeds was stronger than that at low wind speeds, which reflect the impact of heat advection from the urban center. For different LCZs in the main urban area, the strongest UHII, highest risk of HW occurrence, and most significant HWs–UHI interaction were found in the compact mid-rise buildings and compact low-rise buildings (LCZ2 and LCZ3, respectively), followed by the compact high-rise buildings (LCZ1), which was mainly affected by the shading effect of high-rise buildings. The weakest UHII and lowest risk of HW occurrence were found in open high-rise buildings and open mid-rise buildings (LCZ4 and LCZ5, respectively), which generally have good ventilation conditions. Our findings will help to understand urban warming and its association with UHI and HW events in tropical urban regions, which has implications for rational improvement of the urban thermal environment in other tropical urban regions globally.

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Abstract. We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 103 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate.

Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models

Abstract. Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.

Projecting Annual Rainfall Timeseries Using Machine Learning Techniques

Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.

The AntAWS dataset: a compilation of Antarctic automatic weather station observations

Abstract. A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

Integration of shapley additive explanations with random forest model for quantitative precipitation estimation of mesoscale convective systems

Mesoscale convective cloud systems have a small horizontal scale and a short lifetime, which brings great challenges to quantitative precipitation estimation (QPE) by satellite remote sensing. Combining machine learning models and geostationary satellite spectral information is an effective method for the QPE of mesoscale convective cloud, while the interpretability of machine learning model outputs remains unclear. In this study, based on Himawari-8 data, high-density automatic weather station observations, and reanalysis data over the North China Plain, a random forest (RF) machine learning model of satellite-based QPE was established and verified. The interpretation of the output of the RF model of satellite-based QPE was further explored by using the Shapley Additive Explanations (SHAP) algorithm. Results showed that the correlation coefficient between the predicted and observed precipitation intensity of the RF model was .64, with a root-mean-square error of .27 mm/h. The importance ranking obtained by SHAP model is completely consistent with the outputs of random forest importance function. This SHAP method can display the importance ranking of global features with positive/negative contribution values (e.g., current precipitation, column water vapor/black body temperature, cloud base height), and can visualize the marginal contribution values of local features under interaction. Therefore, combining the RF and SHAP methods provides a valuable way to interpret the output of machine learning models for satellite-based QPE, as well as an important basis for the selection of input variables for satellite-based QPE.

Daytime along-valley winds in the Himalayas as simulated by the Weather Research and Forecasting (WRF) model

Abstract. Local along-valley winds in four major valleys on the southern slope of the Nepal Himalayas are studied by means of high-resolution meteorological modelling. The Weather Research and Forecasting model is run with a 1 km horizontal grid spacing covering a 4 d period in December 2014. Model evaluation against meteorological observations from three automatic weather stations in the Khumbu Valley (one of the four valleys) shows a good agreement between the modelled and observed daily cycle of the near-surface wind speed and direction. Well-defined daytime up-valley winds are found in all of the four valleys during this 4 d period. The night-time along-valley winds are weak in magnitude and flow mostly in the up-valley direction. Differences in the daytime up-valley winds are found between the valleys and their parts. As the valleys are under similar large-scale forcing, the differences are assumed to be due to differences in the valley topographies. Parts of the valleys with a steep valley floor inclination (2–5∘) are associated with weaker and shallower daytime up-valley winds compared with the parts that have nearly flat valley floors (<1∘). In the four valleys, the ridge heights also increase along the valley, meaning that the valley floor inclination does not necessarily lead to a reduction in the volume of the valley atmosphere. This way, the dominant driving mechanism of the along-valley winds, within the valleys, could shift from the valley volume effect to buoyant forcing due to the inclination. Two of the valleys have a 1 km high barrier in their entrances between the valley and the plain. Winds at the valley entrances of these two valleys are weaker compared with the open valley entrances. Strong and shallow winds, resembling down-slope winds, are found on the leeward slope of the barrier followed by weaker and deeper winds at the valley entrance, 20 km towards the valley from the barrier. Although the large-scale flow during the 4 d period was similar to the long-term climatology, the impact of different large-scale flows on the thermally driven winds was not considered. This topic could be addressed in the future by performing a longer simulation.

Cause analysis of the extreme hourly precipitation and its relationship with the urban heat island intensity in Shenyang, China

Based on the hourly temperature and precipitation data from China national meteorological stations and regional automatic weather stations in Shenyang, the relationship between extreme hourly precipitation (ExHP) and urban heat island Intensity (UHII) is analyzed. Results show that the UHII is higher at night and in the early morning. The ExHP events mostly occur at night in summer when the UHII is relatively high. The spatial distribution of UHII in Shenyang is consistent with the economic development and the transportation density. Denser population and transportation, and high-rise buildings in the urban center contribute to higher UHII. There are three types of ExHP, namely the abrupt-type ExHP, the growing-type ExHP and the continuous-type ExHP. The overall variation characteristics of the three types of ExHP are relatively consistent. Their UHII values are positive and relatively stable in 6–12 h before the start of ExHP. The UHII begins to increase dramatically about 6 h before the ExHP, but decreases obviously and turn negative after the precipitation begins. Before the abrupt ExHP, the UHII is relatively high and can rapidly return to positive after the ending of ExHP. The UHII of the abrupt-type ExHP is remarkably larger than that of the growing-type and continuous-type ExHP. The UHII before and after the abrupt-type ExHP differs greatly. Before the abrupt ExHP, the UHII is high in the center and low at both ends, and the high-value areas of UHII are mainly located in the urban area. After the abrupt-type EXHP, the UHII drops and turn negative in the whole area. The UHII is obviously increasing with urbanization. The diurnal variation of UHII is enormous, which is higher at night than during the daytime. The increasing UHII can cause abnormal air pressure in cities and villages. The air in the lower atmospheric layer of the city can be heated and expanded, hence resulting in lower local air pressure. Then, the lower air pressure can promote the convergence and upward movement of air, hence facilitating the establishment of UHII circulation. This phenomenon is particularly distinct at night, which is conducive to the occurrence of ExHP events.