Academic literature on the topic 'Cloudy-sky condition'

Abstract The outdoor radon concentration was monitored together with the meteorological parameters at two contrasting complex topographies: sub-Alpine basin (SA) and sub-Mediterranean valley (SM) in winter (December 2017–February 2018) and summer (June–August 2018). The time series for each site and each season are evaluated in three different ways: (i) clear-sky and cloudy condition together, (ii) clear-sky conditions only (cloud cover <20%) and (iii) cloudy condition only (cloud cover >20%), and compared to the expected atmospheric boundary layer (ABL) ‘mixing volume’ caused by meteorological changes. The results have confirmed the sensitivity of diurnal and seasonal radon concentration to the expected ABL ‘mixing volume’ at the two selected sites. The relationship is more pronounced in calm clear-sky conditions. Cloudy conditions are associated with fast weather changes, when the ABL is well mixed and hourly mean radon concentrations do not follow the typical diurnal trend.

With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power.

Daylighting is an important issue in modern architecture that has been characterized by the use of curtain walls in buildings. Nonovercast skies, including clear and partly cloudy days, are essential because they may occur more frequently for places such as in equatorial regions and the tropics. Better understanding of nonovercast sky luminance distribution is vital to estimate the dynamic variation in daylight illuminance as sky condition and solar position change. This paper presents the work on the evaluation of six clear sky and three partly cloudy sky models against three-year (1999–2001) measured Hong Kong sky luminance data. The general features and characteristics for the models were described and assessed. The nonovercast sky conditions were identified using the ratio of zenith luminance (Lz) to diffuse illuminance (Dv) and the ratio of global illuminance (Gv) to the extraterrestrial illuminance (Ev). Subsequent interpretations of the clear skies into high and low turbid types were conducted in conjunction with the cloud cover (CLD) and the luminous turbidity (Tv), and partly cloudy skies were further subdivided into thin and thick cloud modes using sunshine hour (SH) and global irradiance (GSI). A statistical analysis of the models revealed that the Gusev model (i.e., CIE (Internal Commission on Illumination) polluted sky No. 13) and the model by Chen et al. (1999, “Luminance Distribution Model of Intermediate Skies,” Zhaom Ing Gong Chen Xuebao, 10(1), pp. 59–63 (in Chinese)) developed using artificial neural network (ANN) theory with the measured data in Chongqing, China (29.6degN and 106.5degE) showed the best predictions for sky luminance at this location under the clear and partly cloudy sky conditions, respectively.

The characteristics of plateau precipitation and atmosphere, once accurately and comprehensively understood, can be used to inform sound air–water resource development practices. In this study, atmospheric exploration of the Tibet Plateau (TP) was conducted using ground-based microwave radiometer (MWR) data collected during the East Asian summer monsoon. Atmospheric temperature, pressure, humidity, and other variables were gathered under clear-sky, cloudy-sky, and rainy-sky conditions. Statistical characteristics of the air parcel height and stability/convection indices such as convective available potential energy (CAPE) and convective inhibition (CIN) were investigated, with a special focus on the rainy-sky condition. Two retrieval applications for characterizing precipitation, namely short-term precipitation forecast and quantitative precipitation estimation were presented. Results showed that CAPE values in the Darlag region reached extremes around 18:00–20:00 (UTC+8) for cloudy-sky and rainy-sky conditions with corresponding peaks of about 1046.56 J/kg and 703.02 J/kg, respectively. When stratiform or convective–mixed precipitation occurs, the precipitable water vapor (PWV) and CAPE values were generally greater than 1.7 cm and 1000 J/kg, respectively. CAPE values are likely to decrease before the occurrence of precipitation due to the release of the latent heat in the atmosphere.

To estimate global solar radiation from easy available weather forecast data (sky condition), Markov model is used for this estimation. The five-year (1996-2000) global radiation data that are taken at an hour intervals from Nakhon Pathom station, Thailand (latitude 13.81ºN and longitude 100.04ºE) are used to construct the Markov transition matrices. The global radiation sequences in 2000 will be generated by based on the characteristic probability of moving global radiation values which were observed from the obtained data during 1996-1999. The autocorrelation function is used for checking the order of probability of moving obtained data. In this study, the five first and five second-order Markov transition matrices (MTMs), which are selected from the autocorrelation functions, are constructed, each MTMs will be used for generating global radiation values in each day with different sky conditions (clear, partly cloudy, mostly cloudy, cloudy and overcast). From the results of comparison between the statistical characteristics of observed and two synthetic generated data, global radiation data behavior slightly improved by the second order Markov model.

Abstract In this paper, the amount of satellite-derived longwave cloud radiative forcing (CRF) that is due to an increase in upper-tropospheric water vapor associated with the evolution from clear-sky to the observed all-sky conditions is assessed. This is important because the satellite-derived clear-sky outgoing radiative fluxes needed for the CRF determination are from cloud-free areas away from the cloudy regions in order to avoid cloud contamination of the clear-sky fluxes. However, avoidance of cloud contamination implies a sampling problem as the clear-sky fluxes represent an area drier than the hypothetical clear-sky humidity in cloudy regions. While this issue has been recognized in earlier works this study makes an attempt to quantitatively estimate the bias in the clear-sky longwave CRF. Water vapor amounts in the 200–500-mb layer corresponding to all-sky condition are derived from microwave measurements with the Special Sensor Microwave Temperature-2 Profiler and are used in combination with cloud data for determining the clear-sky water vapor distribution of that layer. The obtained water vapor information is then used to constrain the humidity profiles for calculating clear-sky longwave fluxes at the top of the atmosphere. It is shown that the clear-sky moisture bias in the upper troposphere can be up to 40%–50% drier over convectively active regions. Results indicate that up to 12 W m−2 corresponding to about 15% of the satellite-derived longwave CRF in tropical regions can be attributed to the water vapor changes associated with cloud development.

Abstract. Sky condition is a matter of interest for public and weather predictors as part of weather analyses. In this study, we apply a method that uses total solar radiation and other meteorological data recorded by an automatic station for deriving an estimation of the sky condition. The impetus of this work is the intention of the Catalan Meteorological Service (SMC) to provide the public with real-time information about the sky condition. The methodology for deriving sky conditions from meteorological records is based on a supervised classification technique called maximum likelihood method. In this technique we first need to define features which are derived from measured variables. Second, we must decide which sky conditions are intended to be distinguished. Some analyses have led us to use four sky conditions: (a) cloudless or almost cloudless sky, (b) scattered clouds, (c) mostly cloudy – high clouds, (d) overcast – low clouds. An additional case, which may be treated separately, corresponds to precipitation (rain or snow). The main features for estimating sky conditions are, as expected, solar radiation and its temporal variability. The accuracy of this method of guessing sky conditions compared with human observations is around 70% when applied to four sites in Catalonia (NE Iberian Peninsula). The agreement increases if we take into account the uncertainty both in the automatic classifier and in visual observations.Key words. Meteorological and atmospheric dynamics (instruments and techniques; radiative processes) – Atmospheric composition and structure (cloud physics and chemistry)

AbstractAn algorithm was developed to estimate planetary boundary layer (PBL) heights from hourly archived wind profiler data from the NOAA Profiler Network (NPN) sites located throughout the central United States. Unlike previous studies, the present algorithm has been applied to a long record of publicly available wind profiler signal backscatter data. Under clear-sky conditions, summertime averaged hourly time series of PBL heights compare well with Richardson number–based estimates at the few NPN stations with hourly temperature measurements. Comparisons with estimates based on clear-sky reanalysis show that the wind profiler (WP) PBL heights are lower by approximately 250–500 m. The geographical distribution of daily maximum PBL heights corresponds well with the expected distribution based on patterns of surface temperature and soil moisture. Wind profiler PBL heights were also estimated under mostly cloudy-sky conditions, and are generally comparable to the Richardson number–based PBL heights and higher than the reanalysis PBL heights. WP PBL heights have a smaller clear–cloudy condition difference than either of the other two. The algorithm presented here is shown to provide a reliable summertime climatology of daytime hourly PBL heights throughout the central United States.

Photosynthetic Active Radiation (PAR) is the most important light source for plant photosynthesis. It is known that most of PAR from solar radiation is well absorbed by the surface. The canopy is the surface in forest region, consists an aboveground portion of plant community and formed by plant crowns. On the other hand, incident solar radiation is fluctuating at all times because of fluctuating sky conditions. Therefore, qualitative light environmental measurements in forest are recommended to execute under stable cloudy condition. In fact, it is quite a few opportunities to do under this sky condition. It means that the diffuse light condition without the direct light is only suitable for this measurement. <br><br> In this study, we challenged the characterization the forest light environment as its representativeness under no consideration of sky conditions through analysis huge quantities of instantaneous data which obtained under the different sky conditions. All examined data were obtained under the different sky conditions at the tropical rainforest canopy as one of the typical fluctuating sky conditions regions. An incident PAR is transmitted and scattered by different forest layers at different heights. Various PAR data were measured with quantum units as Photosynthetic Photon Flux Density (PPFD) at different forest heights by the quantum sensors. By comparing PPFDs at different heights with an incident PPFD, relative PPFDs were calculated, which indicate the degree of PPFD decrease from the canopy top to lower levels. As the results of these considerations, daily averaging is confirmed to be cancelled sky fluctuating influences.

Le Alpi sono state colpite dall'intensificarsi della siccità meteorologica negli ultimi anni. A causa delle mutevoli condizioni climatiche, la regione è vulnerabile alle deviazioni nel ciclo dell'acqua, che possono essere osservate nel contesto delle anomalie dell'evapotraspirazione (ET). La temperatura della superficie terrestre (LST) è un fattore chiave nella regolazione dello scambio di acqua ed energia tra terra e atmosfera, che la mette in relazione direttamente con ET. Lo sviluppo di modelli di bilancio energetico a due fonti (TSEB) guidati da dati di telerilevamento termico ha dato un contributo significativo alla stima dell'ET su larga scala. Tuttavia, la loro risoluzione spaziale grossolana e la sensibilità degli strumenti TIR alle condizioni del cielo nuvoloso li rendono insufficienti per ecosistemi complessi, come le regioni di montagna. Per superare queste limitazioni, questa tesi è servita per sviluppare un nuovo prodotto della temperatura della superficie terrestre in cielo sereno con una risoluzione spaziale di 250 m, in alternativa ai dati MODIS LST di 1 km, per la stima dei flussi TSEB a risoluzione fine. Nella prima parte della tesi, lo squilibrio tra la risoluzione spaziale dei dati MODIS LST di 1 km è stato risolto applicando una procedura di sharpening per ottenere LST giornaliero a una risoluzione spaziale di 250 m. A causa delle ridotte capacità dei modelli statistici LST-VNIR in ecosistemi complessi, sono stati utilizzati predittori multi-sorgente, tra cui l'indice di NDVI e il DEM. Ispirato dalla superiorità dell'apprendimento automatico per problemi non lineari, sono state sfruttate le relazioni tra LST a risoluzione grossolana e predittori di 250 m con algoritmo a foresta casuale (RF). I risultati ottenuti indicano un miglioramento del 20% nell'accordo tra Landsat e l'affilato LST rispetto alle statistiche per il set di dati MODIS originale. I modelli LST hanno determinato un RMSE medio di 2,3°C e un MAE di 1,8°C. Al fine di ricostruire gli LST mancanti sotto le nuvole, l'autore ha proposto un nuovo approccio per prevedere i pixel non validi sfruttando la correlazione tra LST a terra e temperatura dell'aria in combinazione con variabili ausiliarie, ad esempio radiazione solare discendente, albedo e LAI sotto cieli nuvolosi a lungo termine. Considerando un'elevata dipendenza del sito determinata da diversi tipi di copertura del suolo, la ricostruzione LST è stata eseguita per stazioni aggregate che rappresentano tre gruppi di vegetazione: praterie, foreste e colture permanenti. Il gap-filling è stato eseguito con due passaggi: modellazione LST basata sul sito da variabili derivate dal suolo sotto cieli nuvolosi e quindi applicazione dei modelli adattati a predittori a griglia a livello di subpixel corrispondenti all'output ridotto. La ricostruzione ha ottenuto prestazioni affidabili con dati locali che producono R2 di 0,84 e RMSE di 2,12°C. Nell'ultima parte della tesi, le mappe LST risultanti sono state incorporate nel modello di bilancio energetico a due fonti di Priestley-Taylor per la stima dei flussi energetici a una risoluzione spaziale di 250 m. Innanzitutto, le prestazioni del modello forzato dalle temperature locali sono state valutate con flussi misurati da torri di eddy covariance. Le simulazioni di riferimento per il calore latente (LE) e sensibile (H) hanno prodotto un RMSE medio di 57 Wm-2 e un bias assoluto medio (MB) di 26 Wm-2. Successivamente, le stime del modello guidate da LST basati su satellite, ovvero il prodotto LST MODIS originale di 1 km e le mappe ridimensionate, sono state convalidate rispetto ai dati in situ. Flussi turbolenti modellati con LST di 250 m hanno portato a RMSE di 86 Wm-2 e MB di 55 Wm-2, che si sono tradotti in una diminuzione dell'8% e del 15% nei rispettivi errori rispetto alle stime TSEB combinate con l'LST MODIS originale.
The European Alps have been affected by intensification of meteorological droughts in recent years. Due to changing climatic conditions, the region is vulnerable to deviations in water cycling, which can be observed in the context of evapotranspiration (ET) anomalies. Land surface temperature (LST) is a key factor in regulating the exchange of water and energy between land and atmosphere, which directly relates it to ET. Development of two-source energy balance (TSEB) models driven by thermal remote sensing data has made a significant contribution to estimate ET at large scale. However, their coarse spatial resolution and sensitivity of TIR instruments to cloudy-sky conditions make them insufficient for complex ecosystems, such as mountain regions. To overcome these limitations, this thesis served to develop a new clear-sky land surface temperature product at 250 m spatial resolution, as an alternative to 1-km MODIS LST data, for estimating fine-resolution TSEB fluxes. In the first part of the thesis, imbalance between spatial resolution of 1-km MODIS LST data was solved by applying a sharpening procedure to obtain daily LST at 250-m spatial resolution. Due to reduced capabilities of LST–VNIR statistical models in complex ecosystems, multi-source predictors, including normalized difference vegetation index (NDVI) and digital elevation model (DEM) were used. Inspired by superiority of machine learning for non-linear problems, relationships between coarse resolution LSTs and 250-m predictors with random forest (RF) algorithm were exploited. The obtained results indicate an improvement of 20% in the agreement between Landsat and the sharpened LST compared to statistics for the original MODIS dataset. The LST models determined averaged RMSE of 2.3°C and MAE of 1.8°C. In order to reconstruct missing LSTs beneath the clouds, the author proposed a novel approach to predict invalid pixels by exploiting correlation between ground-based LST and air temperature in conjunction with auxiliary variables, e.g., downwelling solar radiation, albedo- and LAI-derived products under long-term cloudy-skies. Considering a high site dependency driven by different land-cover types, LST reconstruction was performed for aggregated stations that represent three vegetation groups: grassland, forest and permanent crops. The gap-filling was performed with two steps: site-based LST modelling from ground-derived variables under cloudy skies, and then applying the fitted models to gridded predictors at subpixel level corresponding to the downscaled output. The reconstruction achieved reliable performance with local data yielding R2 of 0.84 and RMSE of 2.12°C. In the last part of the thesis, the resulting LST maps were incorporated into two-source energy balance model of Priestley-Taylor for estimating energy fluxes at 250-m spatial resolution. First, the performance of the model forced by local temperatures was evaluated with measured fluxes from eddy covariance towers. The benchmark simulations for latent (LE) and sensible heat (H) yielded an averaged RMSE of 57 Wm-2 and mean absolute bias (MB) of 26 Wm-2. Next, the model estimates driven by satellite-based LSTs, i.e., original 1-km MODIS LST product and downscaled maps, were validated against in-situ data. Turbulent fluxes modelled with 250-m LSTs resulted in RMSE of 86 Wm-2 and MB of 55 Wm-2, which translated to 8% and 15% decrease in the respective errors when compared to TSEB estimates combined with original MODIS LST.

Evapotranspiration (ET) is a significant hydrological process. It can be studied and estimated using remote sensing based methods at multiple spatial and temporal scales. Most commonly and widely used remote sensing based methods to estimate actual evapotranspiration (AET) are a) methods based on energy balance equations, b) vegetation coefficient based method and c) contextual methods. These three methods require reflectance and land surface temperature (LST) data measured at optical and thermal portion of the electromagnetic spectrum. However, these data are available only for clear sky conditions and fail to be retrieved under overcast conditions creating gaps in the data, which result in discontinuous of AET product. Moreover, energy balance equation based methods and evaporative fraction (EF) based contextual methods are difficult to apply over overcast conditions. In this context, vegetation coefficient based (Tasumi et al., 2005; Allen et al., 2005) and microwave remote sensing based methods can be applied under cloudy sky conditions (Sun et al., 2012), since microwave radiations can penetrate through clouds, but these data are available at coarse resolution. In the vegetation coefficient method temporal upscaling can be avoided. Therefore in this research vegetation coefficient based method is employed over Cauvery basin to estimate daily AET for clear and cloudy sky conditions. Required critical variables for this method such as reference evapotranspiration (ETo) and vegetation coefficients are obtained using LST and optical vegetation indices for all sky conditions. In this study, all sky conditions refer to both clear and cloudy sky conditions. Most important variable for estimation of ETo using radiation and temperature based models is air temperature (Ta). In this study, for better accuracy of Ta, two satellite based approaches namely, Temperature Vegetation Index (TVX) and Advance Statistical Approaches (ASA) were evaluated. In the TVX approach, in addition to traditional Normalized Difference Vegetation Index (NDVI), other vegetation indices such as Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI) were also examined. In case of ASA, bootstrap technique was used to generate calibration and validation samples and Levenberg Marquardt algorithm was used to find the solution of the models. The better of the Ta results obtained out of these two approaches were employed in the ETo models and are referred as Ta based ETo models. Instead of Ta, processed LST data obtained directly from the satellite (Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS)) was applied in the ETo models and these are referred as LST based ETo models. These Ta and LST based Hargreaves-Samani (H-S), Makkink (Makk) and Penman Monteith Temperature (PMT) models were evaluated by comparing with the FAO56 PM model. Additionally, simple LST based equation (SLBE) proposed by Rivas et al. (2004) was also examined. Required solar radiation (Rs) data for ETo estimation was obtained from Kalpana1/VHRR satellite data. Results implied that, Ta based PMT model performed better than the Ta based H-S, Makk and SLBE with less RMSE, MAPE and MBE values for all land cover classes and for various climatic regions for clear sky conditions. LST based H-S, PMT, Makk and Ta based Makkink advection models predominantly overestimated ETo for the study region. In the case of TVX approach, to estimate maximum Ta (Tmax), GVMI performed better than NDVI and EVI. Nevertheless, TVX approach poorly estimated Tmax in comparison with statistical approach. ASA performed better for both Tmax and minimum Ta. This study demonstrates the applicability of satellite based Ta and ETo models by considering very few variables for clear sky conditions. Spatially distributed vegetation coefficients (Kv) data with high temporal resolution is another important variable in vegetation coefficient method for daily AET estimation and also it is in demand for crop condition assessment, irrigation scheduling, etc. But available Kv models application hinders because of two main reasons i.e 1) Spectral reflectance based Kv accounts only for transpiration factor but not evaporation, which fails to account for total AET. 2) Required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the Kv data. Hence there is a necessity of a model which accounts for both transpiration and evaporation factors and also gap filling method, which produces accurate continuous quantification of Kv values. Therefore, different combinations of EVI, GVMI and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain best model. This best Kv model had been compared with Guershman et al. (2009) Kv model. To fill the gaps in the data, initially, temporal fitting of Kv values have been examined using Savitsky-Goley (SG) filter for three years of data (2012 to 2014), but this fails when sufficient high quality Kv values were unavailable. In this regard, three gap filling techniques namely regression, Artificial Neural Networks (ANNs) and interpolation techniques have been analyzed. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate Kv values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations and also yielded better results than Guershman et al. (2009) Kv model. Furthermore, the results indicated that SG filter can be used for temporal fitting and for filling the gaps, regression technique can be used as it performed better than other techniques for Berambadi station. Land Surface Temperature (LST) with high spatiotemporal resolution is required in the estimation of ETo to obtain AET. MODIS is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatiotemporal resolution LST under cloudy conditions during daytime and night time without employing in-situ LST measurements. To achieve this, ANN based models were employed for different land cover classes, utilizing MPDI at finer resolution with ancillary data. MPDI was derived using resampled (from 0.250 to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology was quantitatively evaluated through three performance measures namely correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86) K to 4.34(4.00) K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. For night time, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70) K to 2.43(2.12) K and NSE from 0.43 (0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable limits. Under cloudy conditions, results of microwave derived LST were evaluated with Ta which indicated that the approach performed well with RMSE values lesser than the results obtained under clear sky conditions for land cover classes for both day and nighttimes. These predicted LSTs can be applied for the estimation of soil moisture in hydrological studies, in climate studies, ecology, urban climate and environmental studies, etc. AET was estimated for all sky conditions using vegetation coefficient method. Essential parameter ETo under cloudy conditions was estimated using LST and Ta based PMT and H-S models and required solar radiation (Rs) in these two models estimated using equation proposed by Samani (2000). In this equation it was found that the differences between LSTmax or Tmax and LSTmin or Tmin could able to capture the variations due to cloudy sky conditions and hence can be used for estimating ETo under cloudy sky conditions. Results revealed that the estimated Rs correlated well with observed Rs for Berambadi station under cloudy conditions for the year 2013. PMT based ETo values were corresponded with observed ETo under cloudy sky condition. The difference between LST and Ta was less during cloudy conditions, therefore LST or Ta can be used as the only input in temperature based PMT model to estimate ETo. AET estimated correlated well with the observed AET values for clear and cloudy sky conditions. In addition, AET estimated using vegetation coefficient method was compared with two source energy balance (TSEB) method developed by Nishida et al. (2003) under clear sky conditions. It was found that the improved vegetation coefficient method performed better than the TSEB method for Berambadi station. Other microwave vegetation indices such as Microwave Vegetation Indices (MVIs) and Emissivity Difference Vegetation Index (EDVI) are available in literature. Therefore in this study, MVIs are used to predict LST under cloudy conditions using proposed methodology to check whether the MVIs could yield better LST values. Results showed that MPDI performed better than MVIs to predict LST under cloudy sky conditions. Furthermore, MPDI obtained using dual polarizations of 37 GHz channel Tb has advantage of having fine spatial resolution compared to MVIs, as it requires Tb of 19 GHz in addition to Tb of 37 GHz channel which is of coarse resolution and therefore uncertainties resulting from re-sampling technique can be minimized. x

Recent developments in the Weather Research and Forecasting (WRF) Model have made it possible to accurately approximate solar power through the implementation of WRF-Solar. This study couples the WRF-Solar module with a multilayer urban canopy and building energy model in New York City (NYC) to create a unified WRF forecasting model called uWRF-Solar. Hourly time resolution forecasts are validated against ground station data collected at eight different sites. The validation is carried out independently for two different sky conditions: clear and cloudy. Results indicate that the uWRF-Solar model can forecast solar irradiance considerably well for the global horizontal irradiance (GHI) with an R squared value of 0.93 for clear sky conditions and 0.76 for cloudy sky conditions. Results are further used to directly forecast solar power production in the NYC region, where a power evaluation is done at a city scale. The outputs show a gradient of power generation produced by the potential available solar energy on the entire uWRF-Solar grid. In total, for the month of July 2016, NYC had a city PV potential of 233 kW/day/m2 and 7.25 MWh/month/m2.

During November of 1991, a Fourier transform infrared spectrometer was used to measure emission spectra of the sky under both clear and cloudy conditions. Spectral resolution was 1 cm−1 over the region from 500 to 2000 cm−1. These spectra were calibrated by using two blackbody cavities, one at 77 K and the other at ambient temperature. These spectra will be compared with spectra calculated by using radiosonde profiles of the atmosphere and the U.S. Air Force radiative transfer code MODTRAN.