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The fire danger rating system implemented in New Zealand is the Canadian Fire Weather Index (FWI) System developed 40 years ago for Canadian temperate forests. Issues have been raised in relation to this system when applied in other regions with different climate and vegetation environments. For the first time, two methods were proposed for improving the Drought Code (DC) component of the FWI System for New Zealand. The first method (PotE) employs a potential evaporation (PE) scheme that considers wind speed, surface air stability, and water vapor mixing ratio gradient. The second method (soilM) uses soil moisture. For the latter, when soil moisture is derived from observations, the calculated DC represents the actual drought status of the soil. DC and FWI have been calculated with the original and the two new DC methods at 28 climate stations in New Zealand for a pair of 2-yr periods. The Joint U.K. Land Environment Simulator (JULES) was run to provide the PE and soil moisture for the two methods. The original DC method underestimated the drought status in New Zealand, especially in summer, leading to underestimation of FWI. The PotE method significantly overestimated the drought status in summer. The errors in the calculated drought status and FWI were largely reduced by using the soilM method with simulated soil moisture from JULES. In this paper, the reasons for this reduction in error are investigated by testing the sensitivity of DC to surface evaporation and to soil parameters. Potential benefit is found from using the proposed soilM method for monitoring drought status and for FWI calculations. [ABSTRACT FROM AUTHOR]

Retrieval schemes often use two important components: 1) a radiative transfer model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms and 2) a numerical weather prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general, RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally nonlinear residual errors by training a separate preprocessing neural network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the ''flawed'' NWP and RTM models. The final ''compensated/flawed'' retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To the authors'' knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapor from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for nonprecipitating scenes, over land and ocean. Before being inverted, the real observations are ''projected'' into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosonde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapor inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state dependent and adaptable to specific cases (e.g., the same correction will not be applied to medium-range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to project the satellite observations in a reference observational space defined by radiosonde measurements, RTM simulations, or other instrument observational space. [ABSTRACT FROM AUTHOR]

Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94- μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94- μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases. [ABSTRACT FROM AUTHOR]

In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a two-dimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth–Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m-2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m-2 for the NWP analysis to 4 kg m-2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air. [ABSTRACT FROM AUTHOR]

Water vapor plays a key role in the global hydrologic cycle and in climatic change. However, the distribution and variability of water vapor in the troposphere are not understood well—in particular, in China with the complex Tibetan Plateau and the influence of the Asian and Pacific monsoons. In this paper, continuous global positioning system (GPS) observations for 2004–07 in China are used to produce precipitable water vapor (PWV) measurements; these measurements constitute the first investigation of PWV distribution and variability over China. It has been found that the stronger water vapor values are in southeastern China and the lower water vapor values are in northwestern China. These distributions are mainly affected by the latitude, topographical features, the season, and the monsoon. Water vapor variations over China are mainly dominated by seasonal variations. The strong seasonal cycles are in summer with maximum water vapor and in winter with minimum water vapor. The PWV in southeastern China has an annual amplitude of about 15 mm, much larger than in northwestern China at about 4 mm, and meanwhile the time of peak water vapor content is one month earlier than in other regions, probably because of the known rainy season (mei-yu). In addition, significant diurnal variations of water vapor are found over all GPS stations, with a mean amplitude of about 0.7 mm, and the peak value occurs around noon or midnight, depending on geographic location and topographical features. The semidiurnal cycle is weaker, with a mean amplitude of about 0.3 mm, and the first peak PWV value appears around noon. [ABSTRACT FROM AUTHOR]

Atmospheric Infrared Sounder (AIRS; NASA Aqua platform) observations over land are interpreted in terms of monthly mean surface emissivity spectra at a resolution of 0.05 μm and skin temperature. For each AIRS observation, an estimation of the atmospheric temperature and water vapor profiles is first obtained through a proximity recognition within the thermodynamic initial guess retrieval (TIGR) climatological library of about 2300 representative clear-sky atmospheric situations. With this a priori information, all terms of the radiative transfer equation are calculated by using the Automatized Atmospheric Absorption Atlas (4A) line-by-line radiative transfer model. Then, surface temperature is evaluated by using a single AIRS channel (centered at 12.183 μm) chosen for its almost constant emissivity with respect to soil type. Emissivity is then calculated for a set of 40 atmospheric windows (transmittance greater than 0.5) distributed over the AIRS spectrum. The overall infrared emissivity spectrum at 0.05-μm resolution is finally derived from a combination of high-spectral-resolution laboratory measurements of various materials carefully selected within the Moderate-Resolution Imaging Spectroradiometer/University of California, Santa Barbara (MODIS/UCSB) and Advanced Spaceborne Thermal Emission and Reflection Radiometer/Jet Propulsion Laboratory (ASTER/JPL) emissivity libraries. It is shown from simulations that the accuracy of the method developed in this paper, the multispectral method (MSM), varies from about 3% around 4 μm to considerably less than 1% in the 10–12-μm spectral window. Three years of AIRS observations (from April 2003 to March 2006) between 30°S and 30°N have been processed and interpreted in terms of monthly mean surface skin temperature and emissivity spectra from 3.7 to 14.0 μm at a spatial resolution of 1° × 1°. AIRS retrievals are compared with the MODIS (also flying aboard the NASA/Aqua platform) monthly mean L3 products and with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies baseline-fit method (UW/CIMSS BF) global infrared land surface emissivity database. [ABSTRACT FROM AUTHOR]

Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) is a mesoscale atmospheric analysis system for surface variables. It produces an analysis at the hourly time step using ground data observations. One of SAFRAN’s main features is that it is based on climatically homogeneous zones and is able to take vertical variations into account. Originally intended for mountainous areas, it was later extended to cover France. This paper focuses on the validation of the extended version. The principle of the analysis is described and its quality was tested for five parameters (air temperature, humidity, wind speed, rainfall, and incoming radiation), using Météo-France’s observation network and data of some well-instrumented stations. Moreover, SAFRAN’s rainfall was compared with another analysis, known as analyse utilisant le relief pour l’hydrométéorologie (Aurelhy). Last, two different versions of SAFRAN were compared for mountain conditions. Temperature and relative humidity were well reproduced, presenting no bias. Wind speed was also well reproduced; however, its bias was -0.3 m s–1. The interpolation from the 6-h time step of the analysis to the 1-h time step was one of the sources of error. The precipitation analysis was robust and not biased; its root-mean-square error was 2.4 mm day-1. This error was mainly due to the spatial heterogeneity of the precipitation within the geographical zones of analysis (1000 km2). The analysis of incoming solar radiation presented some biases, especially in coastal areas. The results of the comparison with some well-instrumented sites were encouraging. SAFRAN is being run operationally at Météo-France on a real-time basis for various applications. [ABSTRACT FROM AUTHOR]

Typical diurnal wind patterns and their relationship to transport of atmospheric aerosol in the Columbia River gorge of Oregon and Washington are addressed in this paper. The measurement program included measurements of light scattering by particles (bsp) with nephelometers, and wind speed and direction, temperature, and relative humidity at seven locations in the gorge. Winds are shown to respond to along-gorge pressure gradients, and five common patterns were identified: strong, moderate, and light westerly (west to east), light easterly, and winter easterly. The strong westerly and winter easterly patterns were the most common summer and winter patterns, respectively, and represented strong gap flow. The light westerly and light easterly patterns occurred most frequently in spring and autumn transition periods. Winter easterly had the highest light scattering and indicated sources east of the gorge mainly responsible for haze. During summer, as westerly winds increased diurnally, a pulse of hazy air from the Portland, Oregon, metropolitan area is transported eastward into the gorge, arriving later with distance into the gorge. During light easterly flow impacts to haze from the city of The Dalles, Oregon, are noted as the wind shifts direction diurnally. [ABSTRACT FROM AUTHOR]