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The Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) is a fast, 1-D radiative transfer model used in numerical weather prediction, calibration/validation, etc. across multiple federal agencies and universities. The key benefit of the CRTM is that it is a satellite simulator. It provides a highly accurate representation of satellite radiances by using the specific sensor response functions convolved with a line-by-line radiative transfer model (LBLRTM). CRTM covers the spectral ranges consistent with all present operational and most research satellites, from visible to microwave. The capability to simulate ultraviolet radiances and support space-based radar sensors is being added over the next two years in CRTM Version 3.0. In addition to simulated radiances, the CRTM also provides Jacobian outputs needed to interpret satellite observations for numerical weather prediction. The Jacobian estimates how changes in geophysical parameters affect simulated measurements from satellite sensors. Using the Jacobian in modeling and weather prediction improves the accuracy and efficiency of data analysis, leading to better weather predictions. The CRTM model's success and growth depend on community contributions and evaluation. To facilitate this, we have made the CRTM highly accessible through modular programming, clear documentation and tutorials, public domain licensing, unfettered public access via Github, and a clear path to operational implementation for innovative research. We encourage and welcome contributions from the community to help us continue to improve the CRTM.

In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies (S4). This O2R environment is critical for successful research-to-operations (R2O) transitions because it allows rigorous tracking, implementation, and merging of any changes necessary (to operational software codes, scripts, libraries, etc.) to achieve the scientific enhancement. So far, the S4 O2R environment, with close to 4,700 computing cores (60 TFLOPs) and 1,700-TB disk storage capacity, has been a great success and consequently was recently expanded to significantly increase its computing capacity. The objective of this article is to highlight some of the major achievements and benefits of this O2R approach and some lessons learned, with the ultimate goal of inspiring other O2R/R2O initiatives in other areas and for other applications.

Despite the importance of water vapor especially in the tropical region, the diurnal variations of water vapor have not been completely investigated in the past due to the lack of adequate observations. Measurements from Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie (SAPHIR) onboard the low inclination Megha-Tropiques satellite with frequent daily revisits provide a valuable dataset for investigating the diurnal and spatial variation of tropospheric relative humidity in the tropical region. In this study, we first transformed SAPHIR observations into layer-averaged relative humidity, then partitioned the data based on local observation time into 24 bins with a grid resolution of one degree. Afterwards, we fitted Fourier series to the binned data. Finally, the mean, amplitude, and diurnal peak time of relative humidity in tropical regions were calculated for each grid point using either the measurements or Fourier series. The results were separately investigated for different SAPHIR channels as well as for relative humidity with respect to both liquid and ice phases. The results showed that the wet and dry regions are, respectively, associated with convective and subsidence regions which is consistent with the previous studies. The mean tropospheric humidity values reported in this study are generally 10 to 15 % higher than those reported using infrared observations which is because of strict cloud screening for infrared measurements. The results showed a large inhomogeneity in diurnal variation of tropospheric relative humidity in tropical region. The diurnal amplitude was larger over land than over ocean and the oceanic amplitude was larger over convective regions than over subsidence regions. The results showed that the diurnal amplitude is less than 10 % in middle and upper troposphere, but it is up to 30 % in lower troposphere over land. Although the peak of RH generally occurs over night or in early morning, there are several regions where the diurnal peak occurs at other times of the day. The early morning peak time is because of a peak in convective activities in early morning. Additionally, a double peak was observed in tropospheric humidity over some regions which is consistent with double peak in precipitation.

Data from hyperspectral infrared sounders are routinely ingested worldwide by National Weather Centers (NWCs). The cloud-free fraction of this data is used for initializing forecasts which include profiles of temperature, water vapor, water cloud and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in NWC models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature and water vapor from ECMWF (European Center for Medium-range Weather Forecasting) were used as input for the RTMs. For non-frozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm(exp -1)have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2616 cm(exp -1) at night are reasonably consistent with results at 900 cm(exp -1). Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2616 cm(exp -1) are inferior to those at 900 cm(exp -1) for daytime calculations.

Satellite-based layer average stratospheric temperature (T) climate data records (CDRs) now span more than three decades and so can elucidate climate variability associated with processes on multiple time scales. We intercompare and analyze available published T CDRs covering at least two decades, with a focus on Stratospheric Sounding Unit (SSU) and Microwave Sounding Unit (MSU) CDRs. Recent research has reduced but not eliminated discrepancies between SSU CDRs developed by NOAA and the UK Meteorological Office. The MSU CDRs from NOAA and Remote Sensing Systems are in closer agreement than the CDR from the University of Alabama in Huntsville. The latter has a previously unreported inhomogeneity in 2005, revealed by an abrupt increase in the magnitude and spatial variability of T anomaly differences between CDRs. Although time-varying biases remain in both SSU and MSU CDRs, multiple linear regression analyses reveal consistent solar, El Nino–Southern Oscillation (ENSO), quasi-biennial oscillation, aerosol, and piecewise-linear trend signals. Together, these predictors explain 80 to 90% of the variance in the near-global-average T CDRs. The most important predictor variables (in terms of percent explained variance in near-global-average T) for lower stratospheric T measured by MSU are aerosols, solar variability, and ENSO. Trends explain the largest percentage of variance in observations from all three SSU channels. In MSU and SSU CDRs, piecewise-linear trends, with a 1995 break point, indicate cooling during 1979–1994 but no trend during 1995–2013 for MSU and during 1995–2005 for SSU. These observational findings provide a basis for evaluating climate model simulations of stratospheric temperature during the past 35 years.

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.

[1] The quality of humidity measurements from global operational radiosonde sensors in upper, middle, and lower troposphere for the period 2000–2011 were investigated using satellite observations from three microwave water vapor channels operating at 183.31±1, 183.31±3, and 183.31±7 GHz. The radiosonde data were partitioned based on sensor type into 19 classes. The satellite brightness temperatures (Tb) were simulated using radiosonde profiles and a radiative transfer model, then the radiosonde simulated Tb's were compared with the observed Tb's from the satellites. The surface affected Tb's were excluded from the comparison due to the lack of reliable surface emissivity data at the microwave frequencies. Daytime and nighttime data were examined separately to see the possible effect of daytime radiation bias on the sonde data. The error characteristics among different radiosondes vary significantly, which largely reflects the differences in sensor type. These differences are more evident in the mid-upper troposphere than in the lower troposphere, mainly because some of the sensors stop responding to tropospheric humidity somewhere in the upper or even in the middle troposphere. In the upper troposphere, most sensors have a dry bias but Russian sensors and a few other sensors including GZZ2, VZB2, and RS80H have a wet bias. In middle troposphere, Russian sensors still have a wet bias but all other sensors have a dry bias. All sensors, including Russian sensors, have a dry bias in lower troposphere. The systematic and random errors generally decrease from upper to lower troposphere. Sensors from China, India, Russia, and the U.S. have a large random error in upper troposphere, which indicates that these sensors are not suitable for upper tropospheric studies as they fail to respond to humidity changes in the upper and even middle troposphere. Overall, Vaisala sensors perform better than other sensors throughout the troposphere exhibiting the smallest systematic and random errors. Because of the large differences between different radiosonde humidity sensors, it is important for long-term trend studies to only use data measured using a single type of sensor at any given station. If multiple sensor types are used then it is necessary to consider the bias between sensor types and its possible dependence on humidity and temperature.

Efficient use of solar radiation needs detailed knowledge of its spatial and temporal variations. Such information can be achieved using interpolating measured irradiance by ground stations. But more reliable results can be obtained by processing geostationary satellite images. Heliosat is an algorithm which has been developed to estimate global horizontal irradiance at ground level from images taken in the visible band by the Meteosat satellites. The aim of this study was to evaluate the Heliosat-II model by using daily global solar irradiation data measured at the four radiometric stations in Iran as well as Meteosat-5 images which are recorded by a spacecraft over 63E. Mean RMSD% and MBD% for all stations were 11.7% and 1.9%, respectively. The mean values of intercept, slope and correlation coefficient were 0.82 (kWh m � 2 ), 1.05 and 0.93, respectively. Seasonally, the maximum RMSD occurs in autumn (22.1%) and the minimum is experienced in spring (8.4%). This accuracy is a great achievement for producing a high quality solar radiation atlas in a country such as Iran with very sparse radiometric network and frequently unreliable measured irradiation data. 2008 Elsevier Ltd. All rights reserved.

The aim of this study was to develop a new and automatic method for controlling the quality of daily global solar radiation, Gd, using sunshine duration hours. The new method has three levels of tests: first, Gd is compared against daily extraterrestrial radiation that is received on a horizontal surface (0.03×God⩽Gd

This study evaluates the performance of 12 models to estimate hourly diffuse solar irradiation on inclined surfaces from those measured on horizontal surfaces. Total solar irradiation incident on a tilted surface consists of three components including: beam, diffuse and reflected from the ground. On a semi-hourly basis, the beam component can be calculated by the ratio of the incidence angle to the solar zenith angle. The reflected component has a small effect on calculations and may be calculated with an isotropic model. In contrast, models for estimating the diffuse component show major differences, which justify the validation study that this paper discusses. Twelve models were tested against recorded south- and west-facing slope irradiances at Karaj (35° 55 ′ N ; 50° 56 ′ E ), Iran. The following models were included: Badescu [Ba], Tian et al. [Ti], Perez et al. [P9], Reindl et al. [Re], Koronakis [Kr], Perez et al. [P8], Skartveit and Olseth [SO], Steven and Unsworth [SU], Hay [Ha], Klucher [Kl], Temps and Coulson [TC], and Liu and Jordan [LJ]. The relative root mean square error (RMSE), for the south-facing surface ranges from 10.16% to 54.89% for the SO and TC models, respectively. For the west-facing surface, RMSE ranges from 30.71% for the P9 model to 63.53% for the TC model. Statistical indices show that all models produce large errors for the west-facing surface. Statistical indices for the south-facing surface show reasonably good agreement with measured data.

In this paper, copula functions are used to model the dependence structure of monthly mean solar radiation R and sunshine duration hours D. The efficiency of five well-known bivariate parametric copula functions, including (1) normal, (2) student’s t, (3) Clayton, (4) Frank, and (5) Gumbel, are evaluated in a seasonal basis for nine radiometric stations in Iran. First, the most appropriate marginal probability distributions for R and D were individually selected from 16 univariate distributions; then, performance of the parametric copulas for modeling the dependence structure of R-D joint empirical probability distribution was assessed using two criteria. Finally, based on appropriate parametric copulas, the joint simulation of marginal variables was accomplished using the conditional sampling technique. The results show that the best marginal distribution fitted on the original data D and R is normal distribution when transformed with Johnson function (in more than a half of cases). Because of hi...

The aim of this study is the evaluation of models that estimate daily global solar radiation on tilted surfaces from that measured on horizontal surfaces. Global solar radiation incident on a tilted plane consists of three components: beam radiation, diffuse radiation and reflected radiation from the ground. The Klein (1977) method, modified by Andersen (1980), was used for estimating direct solar radiation incident on tilted surfaces and an isotropic model was used for estimating reflected solar radiation incident on a tilted plane. In contrast models for the diffuse radiation component show major differences, which justifies a validation study which has been done. Eight models for derivation of daily slope diffuse irradiance from daily horizontal diffuse irradiance were tested against recorded slope irradiances at Karaj (35°55′ N; 50°56′ E), Iran. The following models were included: Badescu (2002), Tian et al. (2001), Reindl et al. (1990), Skartveit and Olseth (1986), Koronakis (1986), Steven and Unsworth (1980), Hay (1979) and Liu and Jordan (1962). All the models use the same method for calculating beam radiation as well as ground reflected radiation. However, only diffuse component of radiation was compared. Statistical indices showed that Reindl’s model gives the most accurate prediction for the south-facing surface and Koronakis’s model performs best for the west-facing surface. The Relative Root Mean Square Errors (%RMSE), except for Steven and Unsworth’s model that has unacceptable results, for whole data range from 1.02 to 10.42%. In general, Reindl’s model produces the best agreement with the measured tilted data.

[1] In the paper “Comparing upper tropospheric humidity data from microwave satellite instruments and tropical radiosondes” by I. Moradi et al. (Journal of Geophysical Research, 115, D24310, doi:10.1029/2010JD013962, 2010), the axis labels in Figure 2 have been confused. [2] The error happened on the part of the authors in the final stages of producing the article and is only in the image itself. Only the labels are interchanged, not the axes. Text and figure caption in the original article refer to the plot as it should look, and as it is included here.

[1] Atmospheric humidity plays an important role in the Earth’s climate. Microwave satellite data provide valuable humidity observations in the upper troposphere with global coverage. In this study, we compare upper tropospheric humidity (UTH) retrieved from the Advanced Microwave Sounding Unit and the Microwave Humidity Sounder against radiosonde data measured at four of the central facilities of the Atmospheric Radiation Measurement program. The Atmospheric Radiative Transfer Simulator (ARTS) was used to simulate satellite brightness temperatures from the radiosonde profiles. Strong ice clouds were filtered out, as their influence on microwave measurements leads to incorrect UTH values. Day and night radiosonde profiles were analyzed separately to take into account the radiosonde radiation bias. The comparison between radiosonde and satellite is most meaningful for data in cloud‐free, nighttime conditions and with a time difference of less than 2 hr. We found good agreement between the two data sets. The satellite data were slightly moister than the radiosonde data, with a mean difference of 1%–2.3% relative humidity (RH), depending on the radiosonde site. Monthly gridded data were also compared and showed a slightly larger mean difference of up to 3.3% RH, which can be explained by sampling issues.

In this study, Advanced Microwave Sounding Unit (AMSU-B) and Microwave Humidity Sounder (MHS) observations are compared to radiosonde data from the Integrated Global Radiosonde Archive (IGRA) dataset. This comparison can be used to evaluate the overall quality of the radiosonde data. Microwave satellite data are influenced by thick ice clouds and radiosonde data show a day-time radiation dry bias. Therefore, we used night-time cloud-free data of the year 2009 for the comparison. Overall, radiosonde data from the former Soviet Union were up to 30 %RH moister than satellite data and the the rest of the world were up to 6 %RH drier than satellite data.