Daily Range Of Air Temperature Research Articles

Glaciological and meteorological investigations of an Alpine debris-covered glacier: the case study of Amola Glacier (Italy)

Debris-covered glaciers are common in many regions of the world, and accurately modelling their melt is of increasing importance for water resources planning, and biological and ecological research. In this study, we investigate meteorological and glaciological conditions and estimate the melt of Amola Glacier (a small debris-covered glacier in the Adamello-Presanella Massif, Italian Alps) using an empirical approach, based on shortwave radiation, surface temperature, debris thickness and thermal resistance. Meteorological conditions are determined from a supraglacial automatic weather station, while the model is calibrated using i) field data acquired during the ablation season 2020, including a network of ablation stakes and thermistors, ii) modelled solar radiation and iii) thermal imagery from Landsat 8 TIRS.The analysis of glacier meteorological conditions shows a high prevalence of cloud-covered (50.60% of daytime observations) and humid conditions, with a high daily air temperature range (22.24 °C). Analysis of thermistor data suggests that a linear thermal gradient of the debris layer can be assumed when the model is run at daily resolution. Modelled debris thickness, surface temperatures and melt capture patterns observed on the field, including the decrease in debris thickness and increasing melt with elevation and their variability across the glacier surface. The root mean square error between measured and observed melt is 0.16 m, corresponding to 22% of the average observed melt, in line with the performance of empirical models for debris-free and debris-covered ice. The model could thus be used to provide a first estimate of debris-covered ice melt for glaciers in the Adamello region. Improvements to the model would require measuring all energy fluxes on the glacier from a weather station and investigating their spatial distribution on the glacier surface.

Effect of Grafting Compatibility on Fruit Yield and Quality of Cantaloupe in a Mediterranean-Type Climate

Grafting is effectively used worldwide to overcome abiotic and biotic factors impacting yield, including soil temperature. Field studies were conducted in 2020 and 2021 in a cool Mediterranean climate (average daily air temperature range of 12.8–17.1 °C) to identify suitable rootstock combinations for grafted cantaloupe (Cucumis melo var. reticulatus) and evaluate fruit yield and quality. Cantaloupe cultivars Sugar Rush (SR), Goddess (G), and Athena (A) were compatible with interspecific hybrid squash (Cucurbita maxima × C. moschata) rootstock cultivars Super Shintosa (SS) and Carnivor (CN) but were incompatible with ‘Carolina Strongback (CS)’ (Citrullus amarus) and ‘Pelop (P)’ (Lagenaria siceraria) rootstocks. Nongrafted cultivars exhibited vine decline at harvest in 2020 but not in 2021, and grafting tended to delay harvest by 15–18 days. Overall, while grafting with interspecific hybrid squash rootstocks may have delayed fruit harvest, fruit quality was not compromised. Further, yield was increased for ‘Goddess’ and ‘Athena’, but not ‘Sugar Rush’. ‘A/CN’ had the highest fruit yield/ha and number per plant. The grafted treatments of each cantaloupe cultivar with interspecific hybrid squash rootstocks met the U.S. fancy grade criteria.

Maximum, Minimum, and Daily Air Temperature Range in Orchards: What Do Observations Reveal?

This study was designed to better understand vegetation’s impact on air maximum (Tmax), minimum (Tmin), and daily temperature range (DTR), as well as seasonality and variability. We selected a flat, under synoptic-scale, northern Serbian region with an operational network of automated weather stations (AWS) for the study. Data were collected directly from the eighteen AWSs placed in the orchard canopy during 2013–2018. Meteorological data, plant phenological data in the form of the BBCH scale, and orchards’ soil characteristics data were collected. Environmental factors influencing the temperature were classified as static (slow or unchangeable) and dynamic (fast-changing). The impact of both factors on maximum, minimum, and daily temperature range and its variability were analyzed. Results show that static factors (like soil texture) affect the annual variation of Tmax, Tmin, and DTR rather than its variability over the season. The dynamic factors, mainly coming from the plant’s phenology, substantially affected the seasonal variability of these variables. Studies like this suffer from missing data and sparse spatial coverage by the AWS network. Therefore, the alternatives of orchard micrometeorological data, nearest climatological station, and ERA5-Land reanalysis data are tested. Both data sets showcased limitations in their applicability, while reanalysis data deviated more from the in-situ measurements, both seasonally and regionally.

Impact of deforestation on radiative and thermal regimes of the territory of Ukraine on the base of global climate models data

This paper is dedicated to the influence of partial deforestation with using global retrospective modelling data from The Land Use Model Intercomparison Project (LUMIP) for the territory of Ukraine. This experiment aims to global gradual deforestation and has two phases. The first phase, defined as the pre-industrial period (1850—1899) with constant unchangeable anthropogenic impact. For this period deforestation modelled with further replacement with grass cover with a linear trend 400000 km2/yr or 20 million km2 per 50 years in general. The second phase is next 30 years with no significant changes in forest cover (1900—1929). For conducting this research the data of several global climate models were applied. The results of analysis have demonstrated that a partial deforestation with grass substitution influences the surface reflectivity or albedo and redistribution of shortwave radiative fluxes. In turn, it provokes changes in thermal regime. It was found that the most significant changes in surface reflectivity and the strongest correlation coefficients between albedo and deforestation are in the winter season due to the presence of snow cover. As a result, statistical significant increase of albedo is with maximum values up to 24 %/50 years in some grids in winter. Then in the summer season maximal changes are up to 2.7 %/50 years due to small differences between forest and grass albedos. As a consequence, changes in albedo cause changes in surface and air temperature regimes. Strong dependencies were found in winter between changes in albedo and temperatures with maximum temperature decrease 2.5…2.0 %/50 years. In warm season correlations are weaker in comparison to cold season, but nevertheless, temperatures decrease also take place with maximum values 2.0…1.5 %/50 years. The analysis between deforestation and daily air temperature range has shown that particularly in winter season there is an increase of 0.5...1.5 %/50 years, whereas such tendency is not observed in warm season. Calculations of year air temperature range demonstrated controversial results among climate models, as follows it is hard to make a conclusion about the contribution of forest cover reduction to changes in this index. It was revealed, that global climate models with higher resolution are more sensitive to changes in albedo and, as a consequence to other characteristics than models with coarse ones. It should be noticed that obtained results concern pre-industrial period with minimal anthropogenic impact, when observed a stable snow cover in winter in Ukraine. In the current climate change with significant warming and reduction of snow season duration deforestation can have opposite effects on radiative and thermal regimes that require further studying.

Thermal effect of the Middle Ural copper smelter (Russia) and growth of birch leaves.

Toxic effects of industrial emissions on vegetation have been extensively studied, and at the same time indirect effects of pollution are less known. In 2011 and 2015, we studied temperature regime and leaf growth for Betula pubescens and B. pendula in deciduous forests near the Middle Ural copper smelter (MUCS). At two polluted sites (1 and 2km from the smelter) and two unpolluted sites (16 and 27km), we logged continuously air temperatures during the growing season (May-August) and measured leaves until completion of growth (May-June). Near MUCS, daily mean air temperatures were 0.7-1.0°C higher with daily temperature range 2.2-2.7°C greater than at distant sites. Daily air temperature range decreased from spring to midsummer, suggesting that the ability of vegetation to mitigate temperature variations increases with plant biomass, which peaks in midsummer. Growth of birch leaves near MUCS began 4-10days earlier and completed 3-7days earlier than far away. Thermal sum over the leaf growth period did not differ between areas in 2011, and in 2015 was lower in the polluted than in the unpolluted area. The earlier leaf growth completion near MUCS can be attributed to higher air temperatures and more rapid accumulation of required thermal sums.

Assessment of heat supply of vegetation period within the northern grain-seeding territory of Kazakhstan

Introduction. The existing agroclimatic handbooks in Kazakhstan are outdated both in informational and technological senses. Therefore necessity of agroclimatic resources reassessment arose.
 Purpose. Research of heat supply of vegetation period within the Northern grain-seeding territory of Kazakhstan.
 Methods. Data of meteorological stations for period of 1981-2014 were used. Methods of statistic and climatologic processing of data were applied. The agroclimatic maps were drafted using ArcGIS 10.1 software.
 Results. The article describes a thermal regime, duration and heat supply of vegetation period within the territory of 4 northern oblasts of Kazakhstan. The territory of Northern Kazakhstan under study hascontinental climate. Average annual air temperature at the area under study increases from the North to the South from 1.8 °C to 5.3 °C. Average July air temperature at the area changes from 18.5 °C to 23.6 °C and average January air temperature – from minus 12.8 °C to minus 17.4 °C. Duration of vegetation period for early spring crops constitutes 172-193 days, for late spring crops – 136-162 days and for warm weather crops – 89-124 days. Daily range of air temperature is 11.4-14.7 °C and that means rather high quality of grain. Sum of active temperatures exceeding 10 °C increases from the North to the South from 2100 °C to 3400 °C. Vegetation period is 90% provided by sum of active temperatures exceeding 10 °C within the range of 2000-2900 °C. Maps of heat supply and vegetation period duration were also drafted.
 Conclusions. In the north of the territory under study thermal sources satisfy demands of soft and common sorts of wheat but are not enough for sunflower and maize, in the south they are enough for wheat, all sorts of sunflower and middle-late sorts of maize.

A Simple New Model for Incoming Solar Radiation Dependent Only on Screen-Level Relative Humidity

AbstractGlobal incoming shortwave radiation (Rg) is the energy source for the majority of biogeochemical processes on Earth as well as for photovoltaic power production. Existing simple site-specific models to estimate Rg commonly use the daily range of air temperature as input variables. Here, the authors present a simple model for incoming shortwave radiation, requiring only screen-level relative humidity data (and site-specific astronomical information). The model was developed and parameterized using high-quality global radiation data covering a broad range of climate conditions. It was evaluated at independent sites, which were not involved in the process of model development and parameterization. The mean 1:1 slope was 1.02 with an averager2of 0.98. Normalized root-mean-square error (NRMSE) averaged at 43%. Despite its simplicity, the new model clearly outperforms conventional approaches, and it comes close to more labor- and data-intensive alternative models.

Comparison Air Temperature under Global Climate Change Issue in Gifu city and Ogaki city, Japan

Increases in air temperature indicate a global climate change. Thus, information in the change of temperature regional scale is important to support global data. The present research was conducted in Gifu city and Ogaki city located in Gifu prefecture, Japan. The results showed that, average air temperatures in both cities are quite similar with a difference value of under 1<sup>o</sup>C. Maximum air temperature in Gifu city is significantly higher than Ogaki city, whereas minimum air temperature in Gifu city is significantly lower than in Ogaki city. Daily range of air temperature in Gifu city significantly higher than in Ogaki city. In both cities, air temperature relatively increased in three decades. This is because of different in land characteristics in both cities.

기상자료를 이용한 무피복 재배 감자의 수량 예측

1980년부터 2012년까지의 전국 92개소의 기상청 기상자료를 수집하고 전국의 감자 지역적응시험 성적을 수집하였다. 이 데이터들을 활용하여 기상요소가 감자수량에 미쳤던 영향을 평가하고 기상자료를 활용, 감자 수량을 예측해 보고자 하였다. 노지 무피복 재배 수미감자를 대상으로 전국 17지역의 86개 지역적응시험 성적을 추출하여 해당지역의 기상요소들간 상관계수를 조사한 결과, 감자의 상서수량은 파종일부터 50일간의 평균기온, 최고기온 및 일교차와 고도의 상관이 나타났고, 수확 50일전부터 수확일까지의 최고기온과도 고도의 상관이 있었으며, 수확 30일전부터 10일전까지의 강수량, 상대습도, 일조시간 및 강수일수도 높은 상관이 나타났다. 이들 시기별 기상요소들과 감자 상서수량간의 관계를 통계분석 프로그램 SAS를 이용하여 단계분석(Stepwise)한 결과, 다음과 같은 감자 수량예측 모형을 얻을 수 있었다. <TEX>$$y=7.820{\times}Tmax_-1-6.315{\times}Prec_-4+128.214{\times}DR_-8+91.762{\times}DR_-3+643.965$$</TEX> 감자는 품종마다 기상에 대한 반응이 다르고, 기상 이외에도 토양, 비료, 재배방법 등 여러가지 가능한 요인들이 존재하므로 이 모형만으로 우리나라 지역별 감자수량을 정확히 예측할 수는 없겠으나, 기후변화에 적응하는 농업기술개발을 위한 지역별 감자 파종적기 재설정, 재배적지 탐색 등에 활용할 수 있을 것으로 판단된다. We aimed to evaluate the effects of climatic elements on potato yield and create a model with climatic elements for estimating the potato yield, using the results of the regional adjustment tests of potato. We used 86 data of the yield data of a potato variety, Sumi, from 17 regions over 11 years. According to the results, the climatic elements showed significant level of correlation coefficient with marketable yield appeared to be almost every climatic elements except wind velocity, which was daily average air temperature (Tave), daily minimum air temperature (Tmin), daily maximum air temperature(Tmax), daily range of air temperature (Tm-m), precipitation (Prec.), relative humidity (R.H.), sunshine hours (S.H.) and days of rain over 0.1 mm (D.R.) depending on the periods of days after planting or before harvest. The correlations between these climatic elements and marketable yield of potato were stepwised using SAS, statistical program, and we selected a model to predict the yield of marketable potato, which was <TEX>$y=7.820{\times}Tmax_-1-6.315{\times}Prec_-4+128.214{\times}DR_-8+91.762{\times}DR_-3+643.965$</TEX>. The correlation coefficient between the yield derived from the model and the real yield of marketable yield was 0.588 (DF 85).

Influence of Mean and Daily Range of Air Temperature on Seedling Emergence and Establishment of Direct-seeded Paddy Rice under Submerged Conditions in Cold-climate Areas

Influence of Mean and Daily Range of Air Temperature on Seedling Emergence and Establishment of Direct-seeded Paddy Rice under Submerged Conditions in Cold-climate Areas

An auto-calibration procedure for empirical solar radiation models

Solar radiation data are an important input for estimating evapotranspiration and modelling crop growth. Direct measurement of solar radiation is now carried out in most European countries, but the network of measuring stations is too sparse for reliable interpolation of measured values. Instead of direct measurements, solar radiation may be estimated from empirical solar radiation models that employ more commonly measured variables or direct outputs of general and regional circulation models (such as air temperature). Coefficients for these models are site-dependent. This usually implies that they are estimated for stations with direct radiation measurements, but need to be interpolated for other locations. In this paper, we introduce a procedure to auto-calibrate empirical solar radiation models that are based on daily air temperature range, i.e. Bristow and Campbell (1984), and Hargreaves et al. (1985). Meteosat Second Generation data were used to create two static look-up tables of mean cloud cover and clear-sky transmissivity as input for the auto-calibration procedure. We demonstrate that daily solar radiation can be accurately estimated from daily air temperature range measurements without site-specific empirical coefficients that require stations that measure solar radiation. The average relative root mean square error for our auto-calibrated models was comparable to ground-measurement-based calibration; only 1% higher for the Bristow and Campbell model (p < 0.05, n = 126), and 2% higher for the Hargreaves model (p < 0.05, n = 126). The mean bias error, relative mean bias error and the slope of linear regression were not statistically different in comparison to ground-measurement-based calibration for the Bristow and Campbell model. When our new solar radiation retrieval algorithm is used to estimate evapotranspiration, we found similar accuracies when using solar radiation input from ground- and auto-calibration. We conclude that our auto-calibration procedure results in accurate solar radiation retrievals, and requires only daily air temperature time series as input. The same procedure could easily be applied to other empirical solar radiation models.

Retrieving daily global solar radiation from routine climate variables

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Angstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Angstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Angstrom model were less than 0.25 MJ m2 day−1 and 2.25 MJ m2 day−1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Angstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Angstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.

Evaluation of temperature-based global solar radiation models in China

Estimation of global solar radiation ( R s ) from the daily range of air temperature (Δ T) offers an important alternative in the absence of measured R s or sunshine duration because of the wide availability of air temperature data. In this paper, we assessed 16 R s models including modified versions of the Bristow and Campbell (B–C) and the Hargreaves (Harg) models across a wide range of agro-ecological conditions in China. Using long-term data from 15 sites in Northeast, North China Plain and Northwest China, we explored the main factors affecting model parameters and the predictive accuracy and proposed empirical equations to estimate these parameters. Two schemes in calculating Δ T were employed: Δ T 1 (based on single day T min) used in the Harg and Δ T 2 (based on 2-day average of T min) used in the B–C model. Results showed that the original B–C model performed similarly to the best performing modified Harg model, but significantly outperformed the original Harg model with a 4–7% higher accuracy. The common practice of fixing some parameters in the B–C model caused the most significant effect and resulted in a 3–9% lower accuracy than that of the original model. In contrast, modifications had the smallest effect and yielded little improvement and are thus unnecessary. The Δ T scheme had a moderate effect, with Δ T 1 generally resulting in a higher accuracy especially in high altitude areas. This indicates that the effect of cold or warm air advection is negligible in the B–C model even in a temperate climate. The accuracy of the temperature-based models was affected mainly by the magnitude of Δ T, with larger Δ T resulting in higher accuracy. Parameters of the B–C model correlated significantly with many commonly used geographical and meteorological factors, meaning that they are more easily obtainable without the need for calibration and consequently R s measurement. The parameter of the original Harg model correlated insignificantly with all the examined factors, meaning that it has to be calibrated. Main findings in this study are valuable in clarifying the relative impact of different approaches in applications on model accuracy, in demonstrating the advection effect, in identifying the dominant factors of model parameters and thus in increasing their availability.

기후 변화에 따른 영남지역의 벼 출수적기 평가

This study was conducted to analyze the optimum heading period according to the recent climatic change for improvement of rice yield and grain quality in the Yeongnam area. We analyzed climatic elements including daily mean air temperature, daily range of air temperature, sunshine hours, and amount of precipitation from 1996 to 2005 in comparison with those of the 1971 to 2000 normal. Daily mean air temperature and amount of precipitation in the recent 10 years increased, but daily range of air temperature and sunshine hours decreased in comparison with the norm. Also, monthly mean air temperature was lowered remarkably in July and August. The monthly amount of precipitation largely increased in August and September. The daily range of air temperature and sunshine hours were greatly decreased from August to October, Possible cultivation periods for rice in the recent 10 years ranged from 171 days in Boughwa to 228 days in Busan and was expanded about days in comparison with the normal. Optimum heading date by local regions for the maximum climatic yield potential was estimated as July 31 at Bonghwa to September 7 at Busan, Masan, and Tongyeong in the recent 10 years. There was a wide difference in optimum heading date according to local legions of the Yeongnam area. Compared to the normal, optimum heading date in the recent 10 years was delayed about I~8 days in most local regions except Bonghwa, Mungyeong, and Yeongdeok. These results suggested that it is necessary to develop late maturity rice cultivars for producing high yield and quality rice grain due to the recent climatic change. Moreover, it is still more important to select the most suitable cultivation period appropriate to the changed climate of each local region in Yeongnam area.

Net ecosystem CO2 exchange and controlling factors in a steppe—Kobresia meadow on the Tibetan Plateau

Knowledge of seasonal variation of net ecosystem CO2 exchange (NEE) and its biotic and abiotic controllers will further our understanding of carbon cycling process, mechanism and large-scale modelling. Eddy covariance technique was used to measure NEE, biotic and abiotic factors for nearly 3 years in the hinterland alpine steppe—Korbresia meadow grassland on the Tibetan Plateau, the present highest fluxnet station in the world. The main objectives are to investigate dynamics of NEE and its components and to determine the major controlling factors. Maximum carbon assimilation took place in August and maximum carbon loss occurred in November. In June, rainfall amount due to monsoon climate played a great role in grass greening and consequently influenced interannual variation of ecosystem carbon gain. From July through September, monthly NEE presented net carbon assimilation. In other months, ecosystem exhibited carbon loss. In growing season, daytime NEE was mainly controlled by photosynthetically active radiation (PAR). In addition, leaf area index (LAI) interacted with PAR and together modulated NEE rates. Ecosystem respiration was controlled mainly by soil temperature and simultaneously by soil moisture. Q 10 was negatively correlated with soil temperature but positively correlated with soil moisture. Large daily range of air temperature is not necessary to enhance carbon gain. Standard respiration rate at referenced 10°C (R 10) was positively correlated with soil moisture, soil temperature, LAI and aboveground biomass. Rainfall patterns in growing season markedly influenced soil moisture and therefore soil moisture controlled seasonal change of ecosystem respiration. Pulse rainfall in the beginning and at the end of growing season induced great ecosystem respiration and consequently a great amount of carbon was lost. Short growing season and relative low temperature restrained alpine grass vegetation development. The results suggested that LAI be usually in a low level and carbon uptake be relatively low. Rainfall patterns in the growing season and pulse rainfall in the beginning and at end of growing season control ecosystem respiration and consequently influence carbon balance of ecosystem.

The Thermal Effect of Melting Snow/Ice Surface on Lower Atmospheric Temperature

To quantify the thermal effect of melting snow/ice surface on the lower atmospheric temperature, “sensitivity of climate index” (α) is used. This new index indicates the rate of decrease in the sensitivity of climate (β) over the melting snow/ice surface compared to that over the snow/ice-free surface. β (°C/[Wm−2]) is defined as the mean daily air temperature range divided by the cumulative amount of solar radiation, in this study. Micrometeorological observations were carried out in various snow/ice cover situations, including seasonal snow cover, glaciers, and snowpatchs. The observations showed that when melt is occurring, the sensitivity of climate is reduced for all the cases tested, regardless of their geographical and climatic conditions. Using the sensitivity of climate index (α), the thermal effect of surface melt is evaluated and compared among the various regions. Results show that the influence of surface melt on the lower atmosphere increases with the size of the snow/ice area.

The Thermal Effect of Melting Snow/Ice Surface on Lower Atmospheric Temperature

To quantify the thermal effect of melting snow/ice surface on the lower atmospheric temperature, “sensitivity of climate index” (α) is used. This new index indicates the rate of decrease in the sensitivity of climate (β) over the melting snow/ice surface compared to that over the snow/ice-free surface. β (°C/[Wm−2]) is defined as the mean daily air temperature range divided by the cumulative amount of solar radiation, in this study. Micrometeorological observations were carried out in various snow/ice cover situations, including seasonal snow cover, glaciers, and snowpatchs. The observations showed that when melt is occurring, the sensitivity of climate is reduced for all the cases tested, regardless of their geographical and climatic conditions. Using the sensitivity of climate index (α), the thermal effect of surface melt is evaluated and compared among the various regions. Results show that the influence of surface melt on the lower atmosphere increases with the size of the snow/ice area.

A comparison of methods to estimate daily global solar irradiation from other climatic variables on the Canadian prairies

Historic estimates of daily global solar irradiation are often required for climatic impact studies. Regression equations with daily global solar irradiation, H, as the dependent variable and other climatic variables as the independent variables provide a practical way to estimate H at locations where it is not measured. They may also have potential to estimate H before 1953, the year of the first routine H measurements in Canada. This study compares several regression equations for calculating H on the Canadian prairies. Simple linear regression with daily bright sunshine duration as the dependent variable accounted for 90% of the variation of H in summer and 75% of the variation of H in winter. Linear regression with the daily air temperature range as the dependent variable accounted for 45% of the variation of H in summer and only 6% of the variation of H in winter. Linear regression with precipitation status (wet or dry) as the dependent variable accounted for only 35% of the summer-time variation in H, but stratifying other regression analyses into wet and dry days reduced their root-mean-squared errors. For periods with sufficiently dense bright sunshine observations (i.e. after 1960), however, H was more accurately estimated from spatially interpolated bright sunshine duration than from locally observed air temperature range or precipitation status. The daily air temperature range and precipitation status may have utility for estimating H for periods before 1953, when they are the only widely available climatic data on the Canadian prairies. Between 1953 and 1989, a period of large climatic variation, the regression coefficients did not vary significantly between contrasting years with cool-wet, intermediate and warm-dry summers. They should apply equally well earlier in the century.

A simple approach towards estimating solar irradiance

Solar radiation is or vital interest in characterizing an area with respect to its agricultural potential. However, these are not readily available for a large network. An attempt. has been made to deduce solar irradiance from climatic data, such as temperature range.&#x0D; &#x0D; Based on daily data of Pune for 1986-90, a relationship has been developed between atmospheric transmittance and the daily range of air temperature. The model developed has been tested on independent data and found to give fairly accurate estimation of solar irradiance. Nearly 70% of the variation in daily solar radiation could be explained by this simple method. The effect of solar irradiance on microclimate has also been discussed. The model developed has been tested for Hyderabad and Calcutta and found to give encouraging results.

On the relationship between incoming solar radiation and daily maximum and minimum temperature

A relationship between atmospheric transmittance and the daily range of air temperature is developed. The relationship is T t = A[1— exp(— BΔT c )] where T t is the daily total atmospheric transmittance, ΔT is the daily range of air temperature, and A, B, and C are empirical coefficients, determined for a particular location from measured solar radiation data. Tests on three data sets indicate that 70–90% of the variation in daily solar radiation can be accounted for by this simple model.