Louis Garand

Estimation of surface albedo from space: A parameterization for global application

This study proposes a simple parameterization to derive surface broadband albedo from satellite observations, using the results of detailed radiative transfer computations for a variety of atmospheric and surface conditions. The end result is a single equation that directly yields surface albedo from observed albedo at the top of the atmosphere, solar zenith angle, and total precipitable water. It was demonstrated that the parameterization is valid for retrieval of both instantaneous and daily mean surface albedo. Sensitivity tests were conducted for precipitable water, aerosol, CO2, O3, and temperature profile. Preliminary validation using collocated satellite and tower measurements indicates that the absolute accuracy requirement of 5% for climate studies is well satisfied. A global monthly climatology (excluding polar areas) of surface albedo is then developed from 5 years of Earth Radiation Budget Experiment clear-sky satellite data and European Centre for Medium-Range Weather Forecasts humidity analysis data. Examination of month-to-month differences for specific 2.5°×2.5° areas indicate that the absolute random error on monthly estimates is less than 1%. Seasonal variation of surface albedo exceeding 1% can thus be detected. Comparisons with other satellite estimates show much closer agreement than with the values used in some general circulation models and numerical weather prediction models especially over snow/ice and deserts.

Radiance and Jacobian Intercomparison of Radiative Transfer Models Applied to HIRS and AMSU Channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High-resolution Infrared Sounder) and 4 AMSU (advanced microwave sounding unit) channels. Interest in this topic was evident by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However, significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle or more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences.

On Differences in Radiosonde Humidity-Reporting Practices and Their Implications for Numerical Weather Prediction and Remote Sensing

Abstract Radiosonde humidity distributions over the United States, Canada, and Europe are discussed. Striking dry-end and wet-end differences are caused by the lack of international standards in the transformation of relative humidity observations to dewpoint depression and in differing ways of calibrating data taken from the same type of instrument. Differences in sondes used in these regions are also discussed and an example of a dual ascent is shown. Some implications for remote sensing and weather prediction are highlighted.

The Stratospheric Extension of the Canadian Global Deterministic Medium-Range Weather Forecasting System and Its Impact on Tropospheric Forecasts

AbstractA new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment ...

Interchannel Error Correlation Associated with AIRS Radiance Observations: Inference and Impact in Data Assimilation

Abstract The interchannel observation error correlation (IOEC) associated with radiance observations is currently assumed to be zero in meteorological data assimilation systems. This assumption may lead to suboptimal analyses. Here, the IOEC is inferred for the Atmospheric Infrared Radiance Sounder (AIRS) hyperspectral radiance observations using a subset of 123 channels covering the spectral range of 4.1–15.3 μm. Observed minus calculated radiances are computed for a 1-week period using a 6-h forecast as atmospheric background state. A well-established technique is used to separate the observation and background error components for each individual channel and each channel pair. The large number of collocations combined with the 40-km horizontal spacing between AIRS fields of view allows robust results to be obtained. The resulting background errors are in good agreement with those inferred from the background error matrix used operationally in data assimilation at the Meteorological Service of Canada. T...

Toward an Integrated Land–Ocean Surface Skin Temperature Analysis from the Variational Assimilation of Infrared Radiances

Abstract Geostationary Operational Environmental Satellite (GOES)-East and -West window channel radiances are directly assimilated using a 1D variational technique, providing surface skin temperature (Ts) estimates over all surface types (land, water, or ice) from a unique system. This is an important advantage over commonly used regression methods, such as split window. The physical nature of the method allows any combination of channels to be used, and adaptation to new sensors is straightforward. A full month (May 2001) of GOES-8 and -10 data is processed every 6 h; Ts estimates are obtained using radiances from imager channels 4 (11 μm) and 5 (12 μm). Imager channel 2 (3.9 μm) can also be used at night. Surface emissivity maps were constructed from available information based on surface type. The diurnal cycle is studied; its range is on the order of 0.7 K over the ocean. Over land, the diurnal range reaches 30 K for mountainous regions, such as the Rockies or Andes. A full GOES disk image can be proc...

A practical approach for the assimilation of cloudy infrared radiances and its evaluation using airs simulated observations

Abstract A variational estimation procedure for the simultaneous retrieval of cloud parameters and thermodynamic profiles from infrared radiances is proposed. The method is based on a cloud emissivity model which accounts for the frequency dependence of cloud absorption and scattering and possible mixed phase situations. An effective cloud top height and emissivity are assumed. Monte Carlo experiments performed in a 1D‐var assimilation context using simulated Atmospheric Infrared Radiance Sounder (AIRS) observations from 100 channels demonstrate the substantial added value, in theory, of cloudy radiance assimilation as opposed to clear‐channel assimilation. Improved temperature and humidity retrievals are obtained for a broad layer above the cloud as well as below cloud level under partial cloud cover conditions. The impact is most pronounced in broken to overcast situations involving mid‐level clouds. In these situations, the effective cloud top height and emissivity are retrieved with estimated rms errors typically lower than 30 hPa and 3%, respectively. Expected relative errors on the retrieved effective particle size are of the order of 30–50%. The methodology is directly applicable to real hyperspectral infrared data upon inclusion, for local estimation, of the cloud parameters in the Canadian 4D‐var assimilation system.

High-Resolution Satellite Analysis and Model Evaluation of Clouds and Radiation over the Mackenzie Basin Using AVHRR Data

Abstract Both the issues of high-resolution satellite analysis and model evaluation for a region centered on the Arctic Circle (60°–75°N) are addressed. Model cloud fraction, cloud height, and outgoing radiation are compared to corresponding satellite observations using a model-to-satellite approach (calculated radiances from model state). The dataset consists of forecasts run at 15-km resolution up to 30 h and nearly coincident Advanced Very High Resolution Radiometer (AVHRR) imagery during the Beaufort and Arctic Storm Experiment over the Mackenzie Basin for a monthly period in the fall of 1994. A cloud detection algorithm is designed for day and night application using the 11-μ channel of AVHRR along with available information on atmospheric and ground temperatures. The satellite and model estimates of cloud fraction are also compared to observations at 20 ground stations. A significant result of the validation is that the model has a higher frequency of low cloud tops and a lower frequency of midlevel...

A Pattern Recognition Technique for Retrieving Humidity Profiles from Meteosat or GOES Imagery

Abstract A retrieval technique based on cloud classification is designed to derive humidity profiles from Meteosat visible (VIS), infrared window (IR), and water vapor (WV) channels, or equivalent sensors available on other satellites. Dewpoint depression (DPD) is the variable retrieved at six standard levels: 1000, 850, 700, 500, 400, and 300 mb. Collocation of soundings and Meteosal-2 imagery was obtained over Europe for March, June, and July 1988. Results are derived from over 2000 dependent and 1000 independent samples. It is found that a classification in seven (IR only) or nine (VIS-IR) classes contains the essential information on cloud type for the application sought. Measures were extracted from approximately 8-km pixel resolution images on 80-km × 80-km and 160-km × 160-km arm, little dependency on horizontal scale was found for the mean humidity profiles associated with each cloud class. The WV channel proved very useful in improving DPDs at higher levels while the VIS channel improved inferenc...

A structural-stochastic model for the analysis and synthesis of cloud images

Abstract A structural-stochastic image model is developed for the analysis and synthesis of cloud images. The ability of the model to characterize the visual appearance of cloud fields observed by satellite with a limited number of parameters is demonstrated. The model merges structural and stochastic information, the stochastic model acting as a local statistical operator applied to the output of the structural model. The structural or large-scale organization of the scene is retrieved from the two-dimensional Fourier representation of the digital image. The pattern generated by the major Fourier components provides a first guess of the scene. The stochastic aspect is described by a Markov model of texture that assumes a binomial probability distribution for the local grey-level variability. This Markov model provides four parameters that represent the clustering strength in the horizontal, vertical and diagonal directions. These parameters are estimated by a standard maximum-likelihood technique. The im...