Abstract

The study deals with analysis of thermal calibration of the Advanced Very High Resolution Radiometer (AVHRR) aboard National Oceanic and Atmospheric Administration (NOAA) spacecrafts. In particular, the effects caused by various types of contamination or corruption of the thermal calibration data are investigated. These phenomena lead to perturbations of the true signal, referred to here as unwanted fluctuations. They must be removed or corrected to maximum possible extent to reduce the error in the calibrated data. It is shown that methods currently employed in operational practice at NOAA and the Canada Centre for Remote Sensing (CCRS) frequently fail to remove some of the unwanted fluctuations in calibration data that may lead to biases in brightness temperature exceeding 1 K. A complex method for removing unwanted fluctuations in the thermal calibration data specifically designed for the AVHRR radiometers is proposed. The procedure is based on combining robust statistical procedures and Fourier transform filtering techniques. Application of the method is considered for various components of calibration data: temperature sensors, blackbody, and space count, as well as gain in all thermal channels. High Resolution Picture Transmission (HRPT) data and Global Area Coverage (GAC) data are analyzed. Power spectra analysis of the calibration data has been conducted to estimate impact of various frequency harmonics. The method proposed may be useful for the development of calibration techniques for similar radiometers and the future National Polar-Orbiting Operational Environmental Satellite System.

Abstract

A method is introduced for inferring cloud optical depth τ from solar radiometric measurements made on an aircraft at altitude z. It is assessed using simulated radiometric measurements produced by a 3D Monte Carlo algorithm acting on fields of broken boundary layer clouds generated from Landsat imagery and a cloud-resolving model. The method uses upwelling flux and downwelling zenith radiance measured at two solar wavelengths where atmospheric optical properties above z are very similar but optical properties of the surface–atmosphere system below z differ. This enables estimation of cloud reflectance into nadir for upwelling diffuse flux and, finally, τ above z. An approximate one-dimensional radiative Green's function is used to roughly account for horizontal transport of photons in all, even broken, clouds. This method is compared to its surface-based counterpart and shown to be superior. Most notably, the aircraft-based approach deals easily with inhomogeneous land surfaces, is less susceptible to poor sampling, and need not account for aerosol below z.

The algorithm appears as though it will have little difficulty inferring high-resolution time series of τ ≲ 40 for most (single layer) clouds. For larger values of τ, biases emerge; particularly, underestimation for the statistically infrequent interiors of cumuliform clouds as photon leakage through cloud sides is not addressed. For the cumuliform and stratiform clouds used here, mean bias errors for retrieved τ are ∼1 (or ∼15%) and ∼0.3 (or ∼3%), respectively. For stratiform clouds with textured bases, performance is likely to improve slightly for flights just up from mean cloud base.

Abstract

Humidity of air is a key environmental variable in controlling the stomatal conductance (g) of plant leaves. The stomatal conductance–humidity relationships employed in the Ball–Woodrow–Berry (BWB) model and the Leuning model have been widely used in the last decade. Results of independent evaluations of the two models vary greatly. In this study, the authors develop a new diagnostic parameter that is based on canopy water vapor and CO₂ fluxes to assess the response of canopy g to humidity. Using eddy-covariance flux measurements at three boreal forest sites in Canada, they critically examine the performance of the BWB and the Leuning models. The results show that the BWB model, which employs a linear relationship between g and relative humidity (h_s ), leads to large underestimates of g when the air is wet. The Leuning model, which employs a nonlinear function of water vapor pressure deficit (D_s ), reduced this bias, but it still could not adequately capture the significant increase of g under the wet conditions. New models are proposed to improve the prediction of canopy g to humidity. The best performance was obtained by the model that employs a power function of D_s , followed by the model that employs a power function of relative humidity deficit (1 − h_s ). The results also indicate that models based on water vapor pressure deficit generally performed better than those based on relative humidity. This is consistent with the hypothesis that the stomatal aperture responds to leaf water loss because water vapor pressure deficit rather than relative humidity directly affects the transpiration rate of canopy leaves.

Abstract

There is a well-recognized spatiotemporal meteorological observation gap at latitudes higher than 55°, especially in the region 55°–70°. A possible solution to address this issue is a constellation of four satellites in a highly elliptical orbit (HEO), that is, two satellites for each polar region. An important satellite product to support weather prediction is atmospheric motion wind vectors (AMVs). This study uses observing system simulation experiments (OSSEs) to evaluate the benefit to forecasts resulting from the assimilation of HEO AMVs covering one or both polar regions. The OSSE employs the operational global data assimilation system of the Canadian Meteorological Center. HEO AMVs are assimilated north of 50°N and south of 50°S. From 2-month assimilation cycles, the study examines the following three issues: 1) the impact of AMV assimilation in the real system, and how this compares to the impact seen in the simulated system, 2) the added value of HEO AMVs in the Arctic on top of what is currently available, and 3) the relative impact of HEO AMVs in the Arctic and Antarctic in comparison with no AMVs. Although the simulated impact of currently available AMVs is somewhat higher than the real impact, a firm conclusion is that the added value of Arctic HEO AMVs is substantial, improving predictability at days 3–5 by a few hours in terms of 500-hPa geopotential height. The impact of HEO AMVs is relatively stronger in the Southern Hemisphere. Forecast validation of atmospheric profiles against the simulated “true” state and against analyses generated within the assimilation cycles yields very similar results beyond 48 h.

Following an overview of the scientific objectives and organization of the French–Russian–German Scanner for Radiation Budget (ScaRaB) project, brief descriptions of the instrument, its ground calibration, and in-flight operating and calibration procedures are given. During the year (24 February 1994–6 March 1995) of ScaRaB Flight Model 1 operation on board Meteor-317, radiometer performance was generally good and well understood. Accuracy of the radiances is estimated to be better than 1% in the longwave and 2% in the shortwave domains. Data processing procedures are described and shown to be compatible with those used for the National Aeronautics and Space Administration's (NASA) Earth Radiation Budget Experiment (ERBE) scanner data, even though time sampling properties of the Meteor-3 orbit differ considerably from the ERBE system orbits. The resulting monthly mean earth radiation budget distributions exhibit no global bias when compared to ERBE results, but they do reveal interesting strong regional differences. The “ERBE-like” scientific data products are now available to the general scientific research community. Prospects for combining data from ScaRaB Flight Model 2 (to fly on board Ressurs-1 beginning in spring 1998) with data from the NASA Clouds and the Earth's Radiant Energy System (CERES) instrument on board the Tropical Rainfall Measurement Mission (TRMM) are briefly discussed.