Routine Meteorological Observations Research Articles

Investigating the influences of wind function parameterization on evaporation estimation using the generalized complementary principle

The complementary principle (CR) is the most practical method for estimating regional evaporation (E) based only on routine meteorological observations. However, CR-based models have certain problems that must be solved for widespread application, such as the influence of wind function types and parameterization. In this study, the generalized CR with polynomial forms (GCR) as a case study is applied to investigate the influences of two different parameterization schemes of wind function—the Penman wind function (fPM) and theoretical wind function (fSM)—on evaporation estimation. The observed daily E values from nine eddy flux stations in Australia with contrasting climates and land cover types were collected to quantify the influences. The results showed that the GCR successfully estimated the E value of flux stations with both the fPM and fSM, and the E value estimated with fSM was more sensitive to wind speed than that with fPM. The estimated aerodynamic term (EA), estimated evaporation error (Eerror), and parameter αc showed marked differences between the two wind functions. The EA-SM (hereafter, subscripts PM and SM represent the wind function type) is approximately 1.05- to 5.06-fold greater than the EA-PM across stations, with a mean of 2.60 ± 1.33 (mean ± standard deviation). The correlation between Eerror-SM with wind speed (u) was positive with an average slope of 0.19, while that of fSM was negative with an average slope of −0.13. The difference in the calibrated parameter of the GCR (i.e., αc) between the two wind functions varied site-to-site, ranging from 0.02 to 0.31. This study highlights that both wind function parameterization schemes successfully estimate regional evaporation, but highly differed in evaporation components, errors, and parameter. These results can help to understand and adopt proper wind parameterization schemes for wider applications of GCR method. Additional studies are required to generalize the spatial variability of different wind function parameters and their influence on the applicability of the GCR.

Comparison of the Visibility Grading Forecast Method Based on Meteorological Factors and Environmental Factors

The main visibility forecast factors were identified with the support of data from routine meteorological observations from the Mianyang Airport and the Mianyang Environmental Monitoring Station from 2015 to 2018, and a visibility grading forecast model was established and tested by dint of the multiple linear regression and the KNN algorithm based on big data mining technology, and the variation characteristics of visibility in winter at the Mianyang Airport were studied. The results showed that (1) in addition to having a significant positive correlation with wind speed, the visibility in winter at the Mianyang Airport has a significant negative correlation with relative humidity, dew point temperature, AQI, PM2.5 concentration, PM10 concentration, and CO, and it has the strongest correlation with relative humidity, and the correlation coefficient is −0.76. (2) The multivariate linear regression model and the KNN model were adopted for grading forecasting experiments on visibility, and the results revealed that both models could be used for visibility grading forecasts. The multiple regression model secures an accuracy of over 70% for forecasts of level 1–5 visibility. In terms of the KNN model, the forecast accuracy is the best when K = 3 or K = 5, notably for level-2, level-4, and level-5 visibility. The forecast accuracy rate is 100% for level-2 visibility, but the forecast for level-1 visibility is poor. (3) The minimum value of the average daily visibility of the Mianyang Airport in winter appeared at 09 : 00 and the maximum value appeared at 17 : 00. The level-1 visibility occurred and developed before 09 : 00 and faded and vanished between 08 : 00 and 15 : 00.

Integration of the generalized complementary relationship into a lumped hydrological model for improving water balance partitioning: A case study with the Xinanjiang model

Lumped hydrological models (LHMs) are useful tools for simulating catchment runoff (Q) owing to their low data requirements, high computational efficiency, and robust performance. However, most LHMs are purposely conceptualized for rainfall-runoff modeling with over-simplified consideration of other water balance components, especially evapotranspiration (E). The generalized complementary relationship (GCR) can reliably estimate the regional E using only routine meteorological observations. Here, a lumped hydrological model (Xinanjiang model, XAJ) was integrated with the GCR (XAJ-GCR) to improve the accuracy of the water balance partitioning of LHMs. Long-term daily observations of Q and E in the Seolmacheon experimental watershed (8.54 km2) in South Korea were collected to test the capability of the XAJ-GCR model to capture Q and E dynamics with three different integration schemes and three different calibration strategies (single or multi-objective). Results show that XAJ-GCR model obtained excellent results in simulating Q (NSEQ = 0.91 ± 0.02) and markedly improves simulating E with NSEE from − 0.35 ± 0.27 (mean ± standard deviation) to 0.56 ± 0.10. The multi-objective calibration strategy achieves better simulation results for Q and E simultaneously with NSEQ = 0.93 and NSEE = 0.69 ± 0.01, in which the built-in structure of the XAJ model is the optimal scheme. This study demonstrated that the integration of GCR into LHMs is feasible and applicable for improving water balance partitioning. The integration maintains all the advantages of LHM and does not increase the model complexity and data requirements.

Determinants and Impact of Fog, Mist and Haze phenomena: The case of Rajiv Gandhi International Airport, Hyderabad

The study aims to analyze the three phenomena - fog, mist, and haze over nine years and their impact on air traffic at Rajiv Gandhi International Airport, Hyderabad. Longer duration events of deteriorated visibility are analysed using surface in-situ data from the airport routine meteorological observations, satellite imageries, backward trajectories, reanalysed data and aerological diagrams. The climatological data predominantly explained long-lasting fog events. The stable layers of the atmosphere, availability of moisture in the planetary boundary layer, light winds, divergence (downward motion/subsidence), and radiational cooling supported the formation of fog and its sustenance. Low visibility disrupted air traffic at the study site by way of flight delays and consequential economic loss.

Long-term daily dataset of surface sensible heat flux and latent heat release over the Tibetan Plateau based on routine meteorological observations

ABSTRACT As the main components of the atmospheric heat source/sink over the Tibetan Plateau (TP), up-to-date spatiotemporal fields of surface sensible heat flux and latent heat release by precipitation are vital for investigating the local land–atmosphere interaction and the effect of the thermal forcing of the TP on global weather and climate. This study recalculates the long-term daily dataset of surface sensible heat flux and latent heat release of condensation over the TP based on 293 routine meteorological observations, with the latest date being 31 December 2019. Most stations have adequate and valid records during the period 1981–2019, and the results for 1951–1980 are also calculated if the observations are available. Moreover, a brief evaluation of the climatology and long-term variation during 1981–2019 is conducted. By providing the most continuous and longest set of observational surface sensible heat flux and latent heat release of condensation data over the TP with a high degree of credibility, this new dataset will support research concerning the multi-timescale variation of diabatic heating/cooling over the TP and its remote influence. It is openly available on the LASG data-sharing platform (http://data.lasg.ac.cn/TPSHLH/).

Decision tree-based detection of blowing snow events in the European Alps

Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Long-term variations of surface solar radiation in China from routine meteorological observations

Accurate observations of long-term surface solar radiation (SSR) are crucial for hydrological processes, agricultural production, and photovoltaic power generation. The main purpose of this study is to investigate the spatial-temporal variations and driving factors of SSR over mainland China. Firstly, ten selected models for estimating SSR were evaluated at 61 China Meteorological Administration (CMA) stations with SSR measurements. Then, the Yang's hybrid model (YHM) with the best model accuracy was used to construct a daily mean SSR dataset at 2474 CMA meteorological stations in China during 1961–2014. The results indicated that the annual mean SSR showed a fluctuating downward trend (−0.16 × 10−1 MJ m−2 year−1) during 1961–2014 under all sky condition. The annual mean SSR showed dimming trend over mainland China during 1961–1989 and 1990–2005, while the annual mean SSR experienced an increasing trend (0.02 × 10−1 MJ m−2 year−1) during 2005–2014 under all sky condition. Meanwhile, the annual mean SSR showed downward trend (−0.02 × 10−1 MJ m−2 year−1) during 1980–2014 under clear sky condition, while an increasing trend (0.06 × 10−1 MJ m−2 year−1) appeared under clear sky conditions from 2005 to 2014. The Qinghai-Tibet Plateau was always the area with the relatively higher SSR values, while the Sichuan Basin was generally the area with the relatively lower SSR values. Finally, the potential relationships between SSR and climate factors were analyzed. The result showed that the variation of annual mean SSR negatively correlates with the optical thickness of all cloud (TC) and the aerosol optical depth (AOD). The SSR had a significant positive correlation with sunshine duration (SD) under all sky conditions. The aerosol loading and SD were the primary factor of variation in clear sky SSR. The research will contribute to the studies related to climate change and land surface process.

Смерчи в российских регионах

New data are presented on tornadoes over land in Russia for the period of 1900–2018 based on various sources. In total, information on 1763 tornadoes was collected, including 993 tornadoes from eyewitness reports on tornado passage and/or associated impacts and 770 tornadoes from satellite data on tornado-induced windthrows. Both single tornadoes and tornado outbreaks, the cases of formation of several tornadoes within one meso- or synoptic-scale system, were reported. On average for 2009– 2018, more than 100 tornadoes are observed in Russia per year, including 15 significant tornadoes (with a wind speed >50 m/s) and one intense tornado (with a wind speed >70 m/s). In some years, these rates can be significantly higher and reach 342, 52, and three tornadoes per year, respectively. Tornadoes are observed on about 41 days per year, up to 68 days per annum in some years. The frequency of occurrence of tornadoes of different categories and the probability of their passage over a point on the ground were estimated. These estimates can be further used to assess a risk of tornado-hazardous situations. The general underestimation of the number of tornadic events in routine meteorological observations and existent statistics is discussed.

A New Long-Term Downward Surface Solar Radiation Dataset over China from 1958 to 2015.

Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000–2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm−2, a root-mean-square-error value of 23.12 Wm−2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970–2015) were also analyzed. The Rs in China showed a significant decline trend (−1.1 Wm−2 per decade) during 1970–2015. A decreasing trend (−2.8 Wm−2 per decade) in Rs during 1970–1992 was observed, followed by a recovery trend (0.23 Wm−2 per decade) during 1992–2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970–2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.

C-FOG: Life of Coastal Fog

AbstractC-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.

Crop yield forecasting based on the satellite monitoring of carbon dynamics in terrestrial ecosystems

A low-parametric model of crop biomass dynamics using the data of satellite remote sensing of terrestrial vegetation index and routine meteorological observations is developed. Modeling is performed with a data filtered by yield-correlation image masking technique. The simulation is based on the Monteith equation for carbon dynamics in terrestrial ecosystems. Meteorological parameters that have an effect on the photosynthesis intensity but rarely measured directly are calculated on the basis of analytical parameterizations obtained from reanalysis data. A validity of the model is demonstrated by the example of satellite monitoring of the spring wheat yield in the regions of the Republic of Belarus.

Observed changes in convective and stratiform precipitation in Northern Eurasia over the last five decades

Long-term changes in convective and stratiform precipitation in Northern Eurasia (NE) over the last five decades are estimated. Different types of precipitation are separated according to their genesis using routine meteorological observations of precipitation, weather conditions, and morphological cloud types for the period 1966–2016. From an initial 538 stations, the main analysis is performed for 326 stations that have no gaps and meet criteria regarding the artificial discontinuity absence in the data. A moderate increase in total precipitation over the analyzed period is accompanied by a relatively strong growth of convective precipitation and a concurrent decrease in stratiform precipitation. Convective and stratiform precipitation totals, precipitation intensity and heavy precipitation sums depict major changes in summer, while the relative contribution of the two precipitation types to the total precipitation (including the contribution of heavy rain events) show the strongest trends in transition seasons. The contribution of heavy convective showers to the total precipitation increases with the statistically significant trend of 1%–2% per decade in vast NE regions, reaching 5% per decade at a number of stations. The largest increase is found over the southern Far East region, mostly because of positive changes in convective precipitation intensity with a linear trend of more than 1 mm/day/decade, implying a 13.8% increase per 1 °C warming. In general, stratiform precipitation decreases over the majority of NE regions in all seasons except for winter. This decrease happens at slower rates in comparison to the convective precipitation changes. The overall changes in the character of precipitation over the majority of NE regions are characterized by a redistribution of precipitation types toward more heavy showers.

Quantifying the Effect of Different Urban Planning Strategies on Heat Stress for Current and Future Climates in the Agglomeration of The Hague (The Netherlands)

In the Netherlands, there will be an urgent need for additional housing by the year 2040, which mainly has to be realized within the existing built environment rather than in the spatial extension of cities. In this data-driven study, we investigated the effects of different urban planning strategies on heat stress for the current climate and future climate scenarios (year 2050) for the urban agglomeration of The Hague. Heat stress is here expressed as the number of days exceeding minimum temperatures of 20 °C in a year. Thereto, we applied a diagnostic equation to determine the daily maximum urban heat island based on routine meteorological observations and straightforward urban morphological properties including the sky-view factor and the vegetation fraction. Moreover, we utilized the Royal Netherlands Meteorological Institute’s (KNMI) climate scenarios to transform present-day meteorological hourly time series into the future time series. The urban planning strategies differ in replacing low- and mid-rise buildings with high-rise buildings (which reduces the sky-view factor), and constructing buildings on green areas (which reduces the vegetation fraction). We found that, in most cases, the vegetation fraction is a more critical parameter than the sky-view factor to minimize the extra heat stress incurred when densifying the neighbourhood. This means that an urban planning strategy consisting of high-rise buildings and preserved green areas is often the best solution. Still, climate change will have a larger impact on heat stress for the year 2050 than the imposed urban densification.

On the relation between outdoor 222Rn and atmospheric stability determined by a modified Turner method

In practice, information about atmospheric stability is often obtained from discrete stability classes determined from routine meteorological observations. However, changing concentrations of the radioactive gas 222Rn present in the atmosphere are also considered a good indicator of vertical dispersion and atmospheric stability. A complex, in-depth analysis between these different approaches of atmospheric stability assessment has not been performed so far, and was the main motivation behind this study. The study presents atmospheric radon data measured in Bratislava (Slovakia) and stability indexes (SI) calculated according to a modified Turner method during a period of one year. Basic features of the diurnal and seasonal variations of these variables are discussed. It was found that the time series of radon activity concentration (RAC) lags approximately 5 h behind that of the Turner stability classes adjusted for temperate climate regions. Various time lags were also identified between RAC and meteorological variables used to determine the stability classes. Evaluation of seasonal trends revealed a low variability of mean monthly values of stability classes compared to the variability of mean monthly values of RAC. Another notable difference between RAC and stability indexes was found – while the stability index can both increase and decrease with wind speed, concentration of outdoor radon was never observed to increase with increasing wind speed. In spite of the mentioned discrepancies, the time series of RAC and SI are generally in a good agreement. This is especially true if one compares the deviations of RAC and SI from their mean daily values, when the differences in their seasonal variability are eliminated. Deviations of RAC can be used to calculate diurnal variations of stability indexes. Analysis of a complete year of data also revealed a roughly linear relationship between average values of RAC and calculated stability indexes, because in large datasets random processes tend to cancel each other out.

Atmospheric heat source/sink dataset over the Tibetan Plateau based on satellite and routine meteorological observations

ABSTRACTThe Tibetan Plateau (TP), acting as a large elevated land surface and atmospheric heat source during spring and summer, has a substantial impact on regional and global weather and climate. To explore the multi-scale temporal variation in the thermal forcing effect of the TP, here we calculated the surface sensible heat and latent heat release based on 6-h routine observations at 80 (32) meteorological stations during the period 1979–2016 (1960–2016). Meanwhile, in situ air-column net radiation cooling during the period 1984–2015 was derived from satellite data. This new dataset provides continuous, robust, and the longest observational atmospheric heat source/sink data over the third pole, which will be helpful to better understand the spatial-temporal structure and multi-scale variation in TP diabatic heating and its influence on the earth’s climatic system.

Attributing the Changes in Reference Evapotranspiration in Southwestern China Using a New Separation Method

Abstract This study investigated monthly and annual reference evapotranspiration changes over southwestern China (SWC) from 1960 to 2012, using the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman–Monteith equation and routine meteorological observations at 269 weather sites. During 1960–2012, the monthly and annual decreased at most sites. Moreover, the SWC regional average trend in annual was significantly negative (p < 0.05); this trend was the same in most months. A new separation method using several numerical experiments was proposed to quantify each driving factor’s contribution to changes and exhibited higher accuracy based on several validation criteria, after which an attribution analysis was performed. Across SWC, the declining annual was mainly due to decreased net radiation (RN). Spatially, the annual changes at most sites in eastern SWC (excluding southeastern West Guangxi) were generally due to RN, whereas wind speed (WND) or vapor pressure deficit (VPD) was the determinant at other sites. Nevertheless, the determinants differed among 12 months. For the whole SWC, increased VPD in February and decreased WND in April, May, and October were the determinant of decreased ; however, decreased RN was the determinant in other months. Overall, the determinant of the monthly changes exhibited a complex spatial pattern. A complete analysis of changes and the related physical mechanisms in SWC is necessary to better understand hydroclimatological extremes (e.g., droughts) and to develop appropriate strategies to sustain regional development (e.g., water resources and agriculture). Importantly, this separation method provides new perspective for quantitative attribution analyses and thus may be implemented in various scientific fields (e.g., climatology and hydrology).

Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data

We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain production areas of Northern Eurasia from 2003–2010. We selected 49 AMSR-E pixels across Ukraine, Russia, and Kazakhstan, based on MODIS land cover percentage data. AMSR-E air temperature growing degree-days (GDD) captures the weekly, monthly, and seasonal oscillations, and well correlated with station GDD. A convex quadratic (CxQ) model that linked thermal time measured as growing degree-days to accumulated growing degree-days (AGDD) was fitted to each pixel’s time series yielding high coefficients of determination (0.88 ≤ r2 ≤ 0.98). Deviations of observed GDD from the CxQ model predicted GDD by site corresponded to peak VI for negative residuals (period of higher latent heat flux) and low VI at beginning and end of growing season for positive residuals (periods of higher sensible heat flux). Modeled thermal time to peak, i.e., AGDD at peak GDD, showed a strong inverse linear trend with respect to latitude with r2 of 0.92 for Russia and Kazakhstan and 0.81 for Ukraine. MODIS VIs tracked similar seasonal responses in time and space and were highly correlated across the growing season with r2 > 0.95. Sites at lower latitude (≤49°N) that grow winter and spring grains showed either a bimodal growing season or a shorter unimodal winter growing season with substantial inter-annual variability, whereas sites at higher latitude (≥56°N) where spring grains are cultivated exhibited shorter, unimodal growing seasons. Sites between these extremes exhibited longer unimodal growing seasons. At some sites there were shifts between unimodal and bimodal patterns over the study period. Regional heat waves that devastated grain production in 2007 in Ukraine and in 2010 in Russia and Kazakhstan appear clearly anomalous. Microwave based surface air temperature data holds great promise to extend to parts of the planet where the land surface is frequently obscured by clouds, smoke, or aerosols, and where routine meteorological observations are sparse or absent.

On the coupling between precipitation and potential evapotranspiration: contributions to decadal drought anomalies in the Southwest China

Under the exacerbation of climate change, climate extreme events, especially for drought, happened frequently and intensively across the globe with greater spatial differences. We used the Standardized Precipitation-Evapotranspiration Index computed from the routine meteorological observations at 269 sites in Southwest China (SWC) to study the drought characteristics (e.g., extent, duration and intensity) and their decadal variations during 1971–2012. It was revealed that the drought, in responses to the coupling between decadal precipitation and potential evapotranspiration (PET) anomalies, differed among regions and periods. For the entire SWC, droughts in 1970s and 2000s+ was generally stronger than in 1980s and 1990s with respect to their spatial extent, duration and intensity, especially in 2000s+. It was well-known that drought was closely related with a lack of precipitation; however, the impact of atmospheric demand of evaporation (reflected by PET here) on drought (e.g., duration and intensity) was rarely paid enough attentions. To that end, a spatial multi-linear regression approach was proposed in this study for quantifying the contributions of decadal PET and precipitation variations to drought duration and intensity. We have found that the contributions of decadal PET anomalies to drought duration and intensity could exceed those of precipitation, e.g., during 1980s and 1990s in SWC. Additionally, despite the strongest droughts in 2000s+, it was suggested that PET could exert comparable impacts on drought anomalies as precipitation. All these findings implied that PET plays a critical role in drought event, which acts to amplify drought duration and intensity. To sum up, this study stressed the need for enough attentions for PET processes in drought studies.

A diagnostic equation for the daily maximum urban heat island effect for cities in northwestern Europe

The urban heat island (UHI) effect, defined as the air temperature difference between the urban canyon and the nearby rural area, is investigated. Because not all cities around the world are equipped with an extensive measurement network, a need exists for a relatively straightforward equation for the UHI effect. Here, we derive a simple, diagnostic equation for the UHI using dimensional analysis. This equation provides a first-order estimation of the daily maximum UHI based on routine meteorological observations and straightforward urban morphological properties. The equation is tested for 14 cities across northwestern Europe and appears to be robust. The comprehensiveness of this analytical equation allows for applications beyond urban meteorological studies.

ARIES, Nainital:A Strategically Important Location for Climate Change Studies in the Central Gangetic Himalayan Region

ARIES, acronym for Aryabhatta Research Institute of Observational Sciences, located in the Central Gangetic Himalayan (CGH) region is emerging as one of the unique sites for climate change studies. The long-term, in situ, precise measurements of aerosols and trace gases obtained from this region provide valuable inputs for climate studies. Atmospheric scientists from ARIES are actively involved in nearsurface measurements for meteorology, aerosols and trace gases as well as vertical profiling. The Institute is also providing the observational infrastructure and research support to three major projects of the Indian Space Research Organization, Geosphere Biosphere Programme, which basically deals with the measurement of aerosols, trace gases and boundary-layer experiments. The upcoming stratosphere-troposphere radar and high-power micro-pulse lidar observational facilities will be utilized for the continuous vertical profiling of winds, aerosol and cloud properties at a very fine resolution in time and space. Apart from this, atmospheric scientists of ARIES also have active national and international research collaborations. The important results obtained from these research activities are highlighted and upcoming major observational facilities in the field of atmospheric sciences are discussed. They clearly demonstrate the importance of the unique geographical location of ARIES for climate change studies in the CGH region. These measurements and routine meteorological observations provide the necessary atmospheric corrections to the astrophysical observations taken using optical telescopes located at the site.