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The Jintsu-oroshi refers to Japan's south foehn, which blows over the Toyama Plain in the Hokuriku region. This region faces the Sea of Japan to the north and the central mountain range to the south. The Jintsu-oroshi occurs more frequently at night than during the day. In this study, we determined the primary factors causing this feature using the Weather Research and Forecasting (WRF) Model. We selected a typical Jintsu-oroshi case in May 2016 for analysis. An extratropical cyclone traversed the Sea of Japan during the event, leading to a temporal change in the synoptic-scale pressure pattern. The observations and numerical simulation results showed that the collapse of the mixed layer over the mountains and the end of the sea breeze are key factors for the nighttime onset of the Jintsu-oroshi. Indeed, mountain waves and their resulting downslope winds did not occur under near-neutral atmospheric stability conditions over the mountains during the daytime. After sunset, the atmospheric stability changed to stable conditions, which caused the downslope winds to blow. However, the downslope winds did not reach the plains because of the sea breeze. After several hours, the sea breeze disappeared, and the downslope winds reached the leeward plains and increased the temperature there. Similar features were confirmed in August 2013 for another typical Jintsu-oroshi case under atmospheric conditions, without temporal changes in the synoptic-scale pressure pattern. We expect the results obtained in this study to advance our understanding of foehn occurrence in regions where mountains adjoin seas, similar to the coastal areas adjacent to the Sea of Japan. Significance Statement: The Jintsu-oroshi refers to Japan's south foehn, which blows over the Toyama Plain in the Hokuriku region. This foehn occurs more frequently at night than during the day. Strong foehns enhance the risk of fire. Nocturnal high temperatures due to foehns can cause sleeplessness in people. Nighttime foehns cause damage to paddy rice. Analyses of observations and numerical simulations for the two typical cases showed that Jintsu-oroshi did not tend to occur during the daytime because the development of a convective boundary layer over the mountains and sea breezes in the leeward plain inhibited the occurrence of the downslope winds. We expect the results obtained in this study to advance our understanding of foehn occurrence in regions where mountains adjoin seas, similar to the coastal areas adjacent to the Sea of Japan. [ABSTRACT FROM AUTHOR]

Gridded climate datasets are used by researchers and practitioners in many disciplines, including forest ecology, agriculture, and entomology. However, such datasets are generally unable to account for microclimatic variability, particularly within sites or among individual trees. One such dataset is a recent climatology of extreme minimum temperatures in the U.S. Great Lakes region, based on the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) gridded temperature dataset. Development of this climatology was motivated by interest in the spatiotemporal variability of winter temperatures potentially lethal to the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive insect that causes mortality of eastern hemlock (Tsuga canadensis). In this study, cold-season daily minimum temperatures were monitored at six Michigan sites varying in latitude, elevation, Great Lakes proximity, and HWA infestation status, to address two objectives. First, we documented the spatiotemporal variability in daily minimum air temperatures recorded at multiple aspects and heights on selected hemlock trees. Second, this variability was characterized in the context of the PRISM extreme minimum temperature climatology. Tree-sensor air temperatures exhibited minimal relationships with aspect but considerable sensitivity to height. Daily minimum temperatures were higher for some tree sensors positioned ≤ 0.2 m above ground level during some time periods, with overall muted temporal variability, relative to an adjacent ambient sensor. This phenomenon was attributed to the insulating effects of snow cover, because the tree–ambient sensor temperature difference was positively correlated with snow depth. Overall, results indicate that such unresolved variability warrants consideration by gridded climate dataset users. [ABSTRACT FROM AUTHOR]

Western Equatorial Africa is one of the least sunny areas in the world. Yet, this has attracted little research so far. As in many other parts of Africa, light availability is mainly estimated using in situ measurements of sunshine duration (SDU). Therefore, this study conducts the first characterization of SDU evolution during the annual cycle for the region. It also evaluates the skill of satellite-based estimates of SDU from the Surface Solar Radiation Data Set–Heliosat, edition 2.1 (SARAH-2.1). Mean annual SDU levels are low: less than 5 h day−1 at the regional scale, with the sunniest stations in the northeast (Cameroon and Central African Republic) and the least sunny in an ∼150-km-wide coastal strip in Gabon and Republic of the Congo (RoC). For most of the stations except the southeast ones in the Democratic Republic of Congo, the lowest SDU levels are recorded in July–September, during the main dry season, with persistent overcast conditions. They are as low as 2.5 h day−1, especially on the windward slopes of the Massifs du Chaillu and du Mayombé, and of the Batéké Plateaus in Gabon and RoC. Although the mean annual and monthly spatial patterns are well reproduced in SARAH-2.1, SDU levels are systematically overestimated by 1–2 h day−1. The largest positive biases are recorded during the December–February dry season, especially at the northernmost stations. Analyses at the daily time scale show that SARAH-2.1 biases arise from a twofold problem: the number of dark days (SDU < 1 h day−1) is 50% lower than observed whereas that of sunny days (SDU > 9 h day−1) is 50% higher than observed. [ABSTRACT FROM AUTHOR]

Utility-scale solar power plants are a rapidly growing component of the renewable energy sector. While most agree that solar power can decrease greenhouse gas emissions, the effects of photovoltaic (PV) systems on surface energy exchanges and near-surface meteorology are not well understood. This study presents data from two eddy covariance observational towers, placed within and adjacent to a utility-scale PV array in southern Arizona. The observational period (October 2017–July 2018) includes the full range of annual temperature variation. Average daily maximum 1.5-m air temperature at the PV array was 1.3°C warmer than the reference (i.e., non-PV) site, whereas no significant difference in 1.5-m nocturnal air temperature was observed. PV modules captured the majority of solar radiation and were the primary energetically active surface during the day. Despite the removal of energy by electricity production, the modules increased daytime net radiation Q* available for partitioning by reducing surface albedo. The PV modules shift surface energy balance partitioning away from upward longwave radiation and heat storage and toward sensible heat flux QH because of their low emissivity, low heat capacity, and increased surface area and roughness, which facilitates more efficient QH from the surface. The PV modules significantly reduce ground heat flux QG storage and nocturnal release, as the soil beneath the modules is well shaded. Our work demonstrates the importance of targeted observational campaigns to inform process-based understanding associated with PV systems. It further establishes a basis for observationally based PV energy balance models that may be used to examine climatic effects due to large-scale deployment. [ABSTRACT FROM AUTHOR]

We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting method, based solely on a vertical temperature difference, as well as a multilinear regression model. The assessment of the wind speed and direction indicates good performance (i.e., wind speed bias of −0.28 m s−1 and 84% of calculated directions stray from observations by less than 45°). Major sources of error are due to the misrepresentation of cross-valley winds and very light winds. The validated method was then successfully applied to a 1-yr period with a similar performance. Potentially, this method could be used to downscale valley wind characteristics for unresolved valleys in mesoscale simulations. [ABSTRACT FROM AUTHOR]

Information on the spatiotemporal rainfall occurrence, its microphysical characteristics, and its reflectivity-rainfall ( Z- R) relations required to provide rainfall mapping based on rain radar data is limited for tropical high mountains. Therefore, this study aims to analyze rainfall types in the Andes cordillera to derive different rain-type Z- R relations using disdrometer observations at three study sites representative for different geographic positions and elevations (2610, 3626, and 3773 m MSL). Rain categorization based on mean drop volume diameter ( D m) thresholds [0.1 < D m (mm) ≤ 0.5; 0.5 < D m (mm) ≤ 1.0; 1.0 < D m (mm) ≤ 2.0] was performed using drop size distribution data at a 5-min time step over an approximate 2-yr period at each location. The findings are as follows: (i) Rain observations characterized by higher (lower) D m and rain rates are more frequent at the lower (higher) site. (ii) Because of its geographic position, very light rain (drizzle) is more common at higher altitudes with longer-duration events, whereas rainfall is more convective at the lower range. (iii) The specific spatial exposition regarding cloud and rain formation seems to play an important role for derivation of the local Z- R relationship. (iv) Low A coefficients (≤60) for the first rain type resemble typical characteristics of orographic precipitation. (v) Greater values of A (lowest and highest stations for D m > 1.0 mm) are attributed to transitional rainfall as found in other studies. (vi) Rain-type Z- R relations show a better adjustment in comparison with site-specific Z- R relationships. This study is the first contribution of Z- R relations for tropical rainfall in the high Andes. [ABSTRACT FROM AUTHOR]

In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2-3 K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas. [ABSTRACT FROM AUTHOR]

Urban areas are believed to affect temperature readings, thereby biasing the estimation of twentieth-century warming at regional to global scales. The precise effect of changes in the surroundings of meteorological stations, particularly gradual changes due to urban growth, is difficult to determine. In this paper, data from 10 temperature stations within 15 km of the city of Mainz (Germany) over a period of 842 days are examined to assess the connection between temperature and the properties of the station surroundings, considering (i) built/paved area surface coverage, (ii) population, and (iii) night light intensity. These properties were examined in circles with increasing radii from the stations to identify the most influential source areas. Daily maximum temperatures Tmax, as well as daily average temperatures, are shown to be significantly influenced by elevation and were adjusted before the analysis of anthropogenic surroundings, whereas daily minimum temperatures Tmin were not. Significant correlations ( p < 0.1) between temperature and all examined properties of station surroundings up to 1000 m are found, but the effects are diminished at larger distance. Other factors, such as slope and topographic position (e.g., hollows), were important, especially to Tmin. Therefore, properties of station surroundings up to 1000 m from the stations are most suitable for the assessment of potential urban influence on Tmax and Tmin in the temperate zone of central Europe. [ABSTRACT FROM AUTHOR]

Data from a network of micrometeorological instruments, mostly mounted 10 m above the roofs of 12 buildings in Washington, D.C., are used to derive average values and spatial differences of the normalized local friction velocity u*/ u ≡ ()1/2/ u (with u being the wind speed reported at the same height as the covariance is measured, w being the vertical wind component, primes indicating deviations, and the overbar indicating averaging). The analysis is extended through consideration of two additional sites in New York City, New York. The ratio u*/ u is found to depend on wind direction for all locations. Averaged values of u*/ u appear to be best associated with the standard deviation of local building heights, with little evidence of a dependence on any other of the modern building-morphology indices. Temperature covariance data show a large effect of nearby activities, with the consequences of air-conditioning systems being obvious (especially at night) in some situations. The Washington data show that older buildings, built largely of native limestone, show the greatest effects of air-conditioning systems. The assumption that the nighttime surface boundary layer is stable is likely to be most often incorrect for both Washington and New York City-the sensible heat flux resulting from heating and cooling of building work spaces most often appears to dominate. [ABSTRACT FROM AUTHOR]

Smoke prediction products are one of the tools used by land management personnel for decision making regarding prescribed fires. This study documents the application to a prescribed fire of a smoke prediction system that employs ARPS-CANOPY, a modified version of the Advanced Regional Prediction System (ARPS) model containing a canopy submodel, as the meteorological driver. In this paper, the performance of ARPS-CANOPY in simulating meteorological fields in the vicinity of a low-intensity fire is assessed using flux-tower data collected prior to and during a low-intensity prescribed fire in the New Jersey Pine Barrens in March 2011. A three-dimensional high-resolution plant area density dataset is utilized to define the characteristics of the canopy, and the fire is represented in ARPS-CANOPY as a heat flux to the atmosphere. The standard ARPS model is compared with reanalysis and upper-air data to establish that the model can simulate the observed synoptic-mesoscale and planetary boundary layer features that are salient to this study. ARPS-CANOPY profiles of mean turbulent kinetic energy, wind speed/direction, and temperature exhibit patterns that appear in the flux-tower observations during both the preburn phase of the experiment and the period of time the flux tower experienced perturbed atmospheric conditions due to the impinging fire. Last, the character and source of turbulence in and around the fire line are examined. These results are encouraging for smoke prediction efforts since transport of smoke from low-intensity fires is highly sensitive to the near-surface meteorological conditions and, in particular, turbulent flows. [ABSTRACT FROM AUTHOR]

This study presents the development of a method to statistically downscale daily wind speed variations in an extended Great Lakes region. A probabilistic approach is used, predicting a daily-varying probability density function (PDF) of local-scale daily wind speed conditioned on large-scale daily wind speed predictors. Advantages of a probabilistic method are that it provides realistic information on the variance and extremes in addition to information on the mean, it allows the autocorrelation of downscaled realizations to be tuned to match the autocorrelation of local-scale observations, and it allows flexibility in the use of the final downscaled product. Much attention is given to fitting the proper functional form of the PDF by investigating the observed local-scale wind speed distribution (predictand) as a function of the decile of the large-scale wind (predictor). It is found that the local-scale standard deviation and the local-scale shape parameter (from a gamma distribution) are nonconstant functions of the large-scale predictor. As such, a vector generalized linear model is developed to relate the large-scale and local-scale wind speeds. Maximum likelihood and cross validation are used to fit local-scale gamma distribution shape and scale parameters to the large-scale wind speed. The result is a daily-varying probability distribution of local-scale wind speed, conditioned on the large-scale wind speed. [ABSTRACT FROM AUTHOR]

Winds through the Vratnik Pass, a mountain gap in the Dinaric Alps, Croatia, are polarized along the gap axis that extends in the northeast-southwest direction. Although stronger northeasterly wind at the Vratnik Pass is frequently related to the Adriatic bora wind, especially at the downstream town of Senj, there are many cases in which the wind at Senj is directionally decoupled from the wind at the Vratnik Pass. A cluster analysis reveals that this decoupling is sometimes related to lower wind speeds or a shallow southeasterly sirocco wind along the Adriatic, but in many cases the bora blows over a wider region, while only Senj has a different wind direction. Several mechanisms can be responsible for the latter phenomenon, including the formation of a lee wave rotor. A numerical model simulation corroborates the decoupling caused by a rotor for a single case. The prevalence of northeasterly winds at the Vratnik Pass during southeasterly sirocco episodes is another result that challenges the current understanding. It is shown that, at least in one of these episodes, this phenomenon is related to a secondary mesoscale low pressure center in the eastern lee of the Apennines that forms as a subsystem of a broader Genoa cyclone. Less frequent southwesterly winds through the gap are predominantly related to the thermal sea breeze and anabatic circulations that are sometimes superimposed on the geostrophic wind. [ABSTRACT FROM AUTHOR]

On an annual basis, heat is the chief cause of weather-related deaths in the United States. Therefore, understanding the temperature structure where people live is important for reducing the health burden imposed by hot weather. This study focused on the air temperatures in the Detroit, Michigan, metropolitan region during the summer of 2009. An observational network was established that included 1) monitors sited in the backyards of residential participants, 2) National Weather Service standard observations, and 3) a network of monitors operated by the State of Michigan. Daily high and low temperatures were analyzed for spatial pattern, magnitude of spatial variability, and relationships with weather conditions. The existence of spatial variability was confirmed specifically during weather that was considered to be dangerous to public health. The relationships between temperature observations and distance to water, distance to city center, and local percent of impervious surface were investigated. The spatial variability during the daily low was typically stronger in magnitude and the spatial pattern was more consistent than were those during the daily high. The largest correlation with land-cover and location attributes was between values of percent of impervious surface and daily low temperatures. Daily high temperatures were most correlated with distance to water. Consistent with previous studies on spatial variability in urban environments, the results suggest a need for sensitivity to the spatially variable nature of exposure to heat events in both public health and urban planning. For example, these results showed that the downtown area experienced elevated temperatures during nights and that the eastern portions of Detroit experienced decreased temperatures during afternoons. [ABSTRACT FROM AUTHOR]

A better quantification of the urban heat islands (UHIs) in the Netherlands is urgently needed given the heat stress-related problems in the recent past combined with the expected temperature rise for the coming decades. Professional temperature observations in Dutch urban areas are scarce, however. Therefore, this research explores the use of observations from weather stations that were installed and maintained by weather amateurs. From a set of over 200 stations, suitable and representative data have been selected from 20 stations, using a set of objective selection criteria that are based on metadata. One year of data (January-December 2010) was considered. From these data, estimates have been obtained of the magnitude of the UHI in Dutch low-rise residential areas. A positive relation (linear model with r2 ≈≈ 0.7) was derived between the summer-averaged UHI and the (neighborhood scale) population density around the observational sites. It was found that the UHI in summer is strongest in nighttime conditions and that it increases with decreasing wind speed, decreasing cloud cover, and increasing sea level air pressure. The summer-averaged UHI was ~0.9°C. During nighttime in a relatively warm 1-month subperiod of the summer, the average UHI was ~1.4°C. During spring and autumn, the UHI was lower than in summer; during winter, no significant UHI was observed. The agreement in results among the different stations and the accordance of the magnitude and variation of the observed UHI with those described in the literature show that automatic observations from weather amateurs can be of sufficient quality for atmospheric research, provided that detailed metadata are available. [ABSTRACT FROM AUTHOR]

The results of numerically modeled wind speed climate, a primary component of wind energy resource assessment in the complex terrain of Croatia, are given. For that purpose, dynamical downscaling of 10 yr (1992-2001) of the 40-yr ECMWF Re-Analysis (ERA-40) was performed to 8-km horizontal grid spacing with the use of a spectral, prognostic full-physics model Aire Limitée Adaptation Dynamique Développement International (ALADIN; the 'ALHR' version). Then modeled data with a 60-min frequency were refined to 2-km horizontal grid spacing with a simplified and cost-effective model version, the so-called dynamical adaptation (DADA). The statistical verification of ERA-40-, ALHR-, and DADA-modeled wind speed on the basis of data from measurement stations representing different regions of Croatia suggests that downscaling was successful and that model accuracy generally improves as horizontal resolution is increased. The areas of the highest mean wind speeds correspond well to locations of frequent and strong bora flow as well as to the prominent mountain peaks. The best results are achieved with DADA and contain bias of 1%% of the mean wind speed for eastern Croatia while reaching 10%% for complex coastal terrain, mainly because of underestimation of the strongest winds. Root-mean-square errors for DADA are significantly smaller for flat terrain than for complex terrain, with relative values close to 12%% of the mean wind speed regardless of the station location. Spectral analyses suggest that the shape of the kinetic energy spectra generally relaxes from k−3 at the upper troposphere to the shape of orographic spectra near the surface and shows no seasonal variability. Apart from the buildup of energy on smaller scales of motions, it is shown that mesoscale simulations contain a considerable amount of energy related to near-surface and mostly divergent meso-β-scale (20-200 km) motions. Spectral decomposition of measured and modeled data in temporal space indicates a reasonable performance of all model datasets in simulating the primary maximum of spectral power related to synoptic and larger-than-diurnal mesoscale motions, with somewhat increased accuracy of mesoscale model data. The primary improvement of dynamical adaptation was achieved for cross-mountain winds, whereas mixed results were found for along-mountain wind directions. Secondary diurnal and tertiary semidiurnal maxima are significantly better simulated with the mesoscale model for coastal stations but are somewhat more erroneous for the continental station. The mesoscale model data underestimate the spectral power of motions with less-than-semidiurnal periods. [ABSTRACT FROM AUTHOR]

Recent developments to the Local-scale Urban Meteorological Parameterization Scheme (LUMPS), a simple model able to simulate the urban energy balance, are presented. The major development is the coupling of LUMPS to the Net All-Wave Radiation Parameterization (NARP). Other enhancements include that the model now accounts for the changing availability of water at the surface, seasonal variations of active vegetation, and the anthropogenic heat flux, while maintaining the need for only commonly available meteorological observations and basic surface characteristics. The incoming component of the longwave radiation ( L↓↓) in NARP is improved through a simple relation derived using cloud cover observations from a ceilometer collected in central London, England. The new L↓↓ formulation is evaluated with two independent multiyear datasets (ŁŁóódźź, Poland, and Baltimore, Maryland) and compared with alternatives that include the original NARP and a simpler one using the National Climatic Data Center cloud observation database as input. The performance for the surface energy balance fluxes is assessed using a 2-yr dataset (ŁŁóódźź). Results have an overall RMSE < 34 W m−−2 for all surface energy balance fluxes over the 2-yr period when using L↓↓ as forcing, and RMSE < 43 W m−−2 for all seasons in 2002 with all other options implemented to model L↓↓. [ABSTRACT FROM AUTHOR]