Abstract

No abstract available.

Abstract

Satellite estimates of surface shortwave radiation (SWR) at high latitudes agree less with ground observations than at other locations; moreover, ground observations at such latitudes are scarce. The comprehensive observations of radiative fluxes made since 1977 by the Department of Energy Atmospheric Radiation Measurement (ARM) Program at the Barrow North Slope of Alaska (NSA) site are unique. They provide an opportunity to revisit accuracy estimates of remote sensing products at these latitudes, which are problematic because the melting of snow/ice and lower solar elevation make the satellite retrievals more difficult.

A newly developed inference scheme for deriving SWR from the Moderate Resolution Imaging Spectroradiometer (MODIS; Terra and Aqua) that utilizes updated information on surface properties over snow and sea ice will be evaluated against these ground measurements and compared with other satellite and model products. Results show that the MODIS-based estimates are in good agreement with observations, with a bias of −5.3 W m⁻² (−4% of mean observations) for the downward SWR, a bias of −5.3 W m⁻² (−7%) for upward SWR, a bias of 1 (1%) for net SWR, and a bias of −0.001 (0%) for surface albedo. As such, the MODIS estimates of SWR can be useful for numerical model evaluations and for estimating the energy budgets at high latitudes.

Abstract

Conventional observations of climate parameters are sparse in space and/or time, and the representativeness of such information needs to be optimized. Observations from satellites provide improved spatial coverage over point observations; however, they pose new challenges for obtaining homogeneous coverage. Surface radiative fluxes, the forcing functions of the hydrologic cycle and biogeophysical processes, are now becoming available from global-scale satellite observations. They are derived from independent satellite platforms and sensors that differ in temporal and spatial resolution and in the size of the footprint from which information is derived. Data gaps, degraded spatial resolution near boundaries of geostationary satellites, and different viewing geometries in areas of satellite overlap could result in biased estimates of radiative fluxes. In this study will be discussed issues related to the sources of inhomogeneity in surface radiative fluxes as derived from satellites, development of an approach to obtain homogeneous datasets, and application of the method to the widely used International Satellite Cloud Climatology Project data that currently serve as a source of information for deriving estimates of surface and top-of-the-atmosphere radiative fluxes. Introduced is an empirical orthogonal function (EOF) iteration scheme for homogenizing the fluxes. The scheme is evaluated in several ways, including comparison of the inferred radiative fluxes with ground observations, both before and after the EOF approach is applied. On the average, the latter reduces the RMS error by about 2–3 W m⁻².

Abstract

The next generation of Geostationary Operational Environmental Satellites (GOES M–Q) will have only one thermal window channel instead of the current two split-window thermal channels. There is a need to evaluate the usefulness of this new configuration to retrieve parameters that presently are derived by utilizing the split-window characteristics. Two algorithms for deriving land surface temperatures (LSTs) from the GOES M–Q series have been developed and will be presented here. Both algorithms are based on radiative transfer theory; one uses ancillary total precipitable water (TPW) data, and the other is a two-channel (3.9 and 11.0 μm) algorithm that aims to improve atmospheric correction by utilizing the middle infrared (MIR) channel. The proposed algorithms are compared with a well-known generalized split-window algorithm. It is found that by adding TPW to the 11.0-μm channel, similar results to those from the generalized split-window algorithm are attained, and the combination of 3.9 and 11.0 μm yields further improvement. GOES M–Q retrievals (simulated with GOES-8 observations), when evaluated against skin temperature observations from the Oklahoma Mesonet, show that with the proposed two-channel algorithm, LST can be determined at an rms accuracy of about 2 K. The proposed algorithms are also applicable for the derivation of sea surface temperatures (SSTs) for which less restrictive assumptions on surface emissivity apply.

Abstract

The geographic and temporal variability of the surface–3600-m cloud frequency and cloud-base height over the contiguous United States for a 5-yr period (2008–12) and the interannual variations for a 16-yr period (2000–15) are described using information from the Automated Surface Observing System (ASOS) observations. Clouds were separated into four categories by the cloud amount reported by ASOS: few (FEW), scattered (SCT), broken (BKN), and overcast (OVC). The geographic distributions and seasonal and diurnal cycles of the four categories of surface–3600-m cloud frequency have different patterns. Cloud frequency of FEW, SCT, and BKN peaks just after noon, whereas the frequency of OVC peaks in the early morning. However, the geographic distributions and seasonal and diurnal cycles of the four categories of the surface–3600-m cloud-base height are similar. The diurnal cycles of the cloud-base height within the surface–3600-m level present a minimum in the morning and peak in the late afternoon or early evening. Cloud frequency and cloud-base height within this range are closely related to surface air temperature and humidity conditions. From 2000 to 2015, the cloud frequency in the contiguous United States showed a positive trend of 0.28% yr⁻¹ while the cloud-base height showed a negative trend of −4 m yr⁻¹ for the surface–3600-m level, accompanied with a positive trend of precipitation days (0.14 days yr⁻¹). Moreover, the increase of cloud frequency and the decrease of cloud-base height were most obvious in winter in the eastern half of the contiguous United States.

Abstract

The first stage in a program of research to develop a regional model capable of describing the hydrology of semiarid areas of northwest Mexico and southwest United States, using remotely sensed data, is described in this paper. Finescale information on cloud cover is required to provide the radiation forcing for making simple, near-real-time estimates of daytime evaporation in hydrologic models, and frequent satellite observations have the potential to document cloud variability at high spatial and temporal resolutions. In this study, the operational framework for obtaining information on cloud cover was developed and applied, using hourly sampled, 1-km resolution GOES-7 data as received in real time in Obregon, Mexico. These satellite data were collected and analyzed from 1 July 1993 to 31 July 1994 for an approximately 10⁶ km² rectangular area in northwest Mexico. An efficient method was devised to provide clear-sky radiance images for the study area, at 4 km × 4 km resolution, and updated at monthly intervals, by applying thresholds indexed to the locally appropriate clear-sky radiance, thereby allowing for spatial and temporal changes in surface conditions. Manual image inspection and comparison with ground-based measurements of cloud cover and surface solar radiation provided reassurance that the high-resolution cloud-screening algorithm gave satisfactory results.

This algorithm was applied to investigate the effects of temporal sampling frequency on estimates of daytime-average cloud cover and to document aspects of the cloud characteristics for the study area. The high-resolution algorithm proved to be efficient and reliable and bodes well for its future use in providing high-resolution estimates of surface solar radiation for use in a hydrologic model. Monthly clear-sky composite images were consistently generated, showing little evidence of contamination by persistent clouds, and tracked the seasonal evolution in surface radiance. Comparison with ground-based measurements gave confidence in the credibility of the satellite estimates and revealed weaknesses in the Campbell–Stokes solarimeter. The seasonal evolution of spatial patterns of cloud and its diurnal cycle were investigated. The average cloudiness for the study area is 0.25, with a substantial annual variation from 0.19 in April to 0.40 in December. Persistent cloudy conditions throughout the year were detected over the Pacific Ocean west of Baja California. The derived high-resolution cloud estimates, when compared with similar estimates from the International Satellite Cloud Climatology Project (ISCCP D1), were about half those obtained with the low-resolution data, indicating that, in this complex study area where land and water boundaries are in close proximity, low-resolution satellite observations of clouds may not be able to depict the true cloud cover.

Abstract

This study focuses on documenting the seasonal progression of the Asian monsoon by analyzing clouds and convection in the pre-, peak-, and postmonsoon seasons. This effort was possible as a result of the movement of Meteosat-5 over the Indian continent during the Indian Ocean Experiment (INDOEX) starting in 1998. The Meteosat-5 observations provide a unique opportunity to study in detail the daytime diurnal variability of clouds and components of the radiation budget. Hourly Meteosat-5 observations are utilized to characterize the Indian monsoon daytime cloud variability on seasonal and diurnal time scales. Distinct patterns of variability can be identified during the various stages of the monsoon cycle. The daytime (0800–1500 LST) diurnal cycle of total cloud amounts is generally flat during the premonsoon season, U shaped during peak-monsoon season, and ascending toward an afternoon peak in the postmonsoon season. Low clouds dominate the Tibetan Plateau and northern Arabian Sea while high clouds are more frequent in the southern Bay of Bengal and Arabian Sea. An afternoon peak in high clouds is most prominent in central India and the Bay of Bengal. Afternoon convection peaks earlier over water than land. Preliminary comparison of cloud amounts from Meteosat-5, International Satellite Cloud Climatology Project (ISCCP) D1, and model output from the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis indicates a large disparity among cloud amounts from the various sources, primarily during the peak-monsoon period. The availability of the high spatial and temporal resolution of Meteosat-5 data is important for characterizing cloud variability in regions where clouds vary strongly in time and space and for the evaluation of numerical models known to have difficulties in predicting clouds correctly in this monsoon region. This study also has implications for findings on cloud variability from polar-orbiting satellites that might not correctly represent the daily average situation.

Abstract

Parameters derived from satellite observations depend on the quality of the calibration method applied to the raw satellite radiance measurements. This study investigates the sensitivity of absolute reflectance, derived cloud cover, and estimated surface shortwave (SW) downward fluxes to two different calibration methods for the visible sensor aboard the eighth Geostationary Operational Environmental Satellite (GOES-8). The first method was developed at NOAA's National Environmental Satellite, Data, and Information Service (NESDIS), and the second at the NASA Langley Research Center. Differences in visible reflectance ranged from −0.5% to 3%. The average difference in monthly mean cloud amount was ∼3%, and the average difference in monthly mean shortwave downward flux was 5 W m⁻². Differences in bias and rms of the SW fluxes when evaluated against ground station measurements were less than 3 W m⁻². Neither calibration method was shown to consistently outperform the other. This evaluation yields an estimate of the errors in fluxes that can be attributed to calibration.

Abstract

We present a global-scale evaluation of surface shortwave (SW↓) radiative fluxes as derived with cloud amount information from the U.S. Air Force (USAF) Cloud Depiction Forecast System (CDFS) II World-Wide Merged Cloud Analysis (WWMCA) and implemented in the framework of the NASA Land Information System (LIS). Evaluation of this product is done against ground observations, a satellite-based product from the Moderate Resolution Imaging Spectroradiometer (MODIS), and several reanalysis outputs. While the LIS/USAF product tends to overestimate the SW↓ fluxes when compared to ground observations and satellite estimates, its performance is comparable or better than the following reanalysis products: ERA5, CFSR, and MERRA-2. Results are presented using all available observations over the globe and independently for several regional domains of interest. When evaluated against ground observations over the globe, the bias in the LIS/USAF product at daily time scale was about 9.34 W m⁻² and the RMS was 29.20 W m⁻² while over the United States the bias was about 10.65 W m⁻² and the RMS was 35.31 W m⁻². The sample sizes used were not uniform over the different regions, and the quality of both ground truth and the outputs of the other products may vary regionally. It is important to note that the LIS/USAF is a near-real-time (NRT) product of interest for potential users and as such fills a need that is not met by most products. Due to latency issues, the level of observational inputs in the NRT product is less than in the reanalysis data.

Abstract

Snow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.