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  • Author or Editor: Michael Uddstrom x
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Michael J. Uddstrom

Abstract

The retrieval of vertical profiles of temperature and water vapor from atmospheric radiances is an ill-posed, nonlinear inversion problem. A linear retrieval estimator must be cast in a form which both minimizes the effects of unmodeled nonlinear processes, and provides retrieval constraints that are pertinent to the sounded atmospheres.

Here, the ill-posed aspect of the problem is resolved by defining a set of meteorologically reasonable retrieval estimator constraints through typical shape function (TSF) classification of a large sample of radiosonde observations. The companion problem of discriminating the TSF constraints to be applied to a particular retrieval estimator, given a set of observed radiances, is investigated. Since the particular linear model chosen to represent the radiance measurements will also have some impact on the retrieval estimator, the effects of errors arising from both simple and simultaneous linearization models for the radiative transfer equation are examined. A TSF constrained, simultaneous, maximum a posteriori retrieval estimator is formulated. Also, a classified, single field-of-view, cloud detection and clear radiance estimator is developed for overcast soundings.

The fundamental properties of the new retrieval estimator are examined and specified via synthetic TOVS radiance data experiments. The retrieval algorithm is also applied to two successive NOAA-7 passes over the New Zealand region, and the retrievals compared with those from a regression retrieval scheme, and operational NWP analysis fields.

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Michael J. Uddstrom
and
Warren R. Gray

Abstract

Twelve months of Southern Hemisphere (maritime) midlatitudes Advanced Very High Resolution Radiometer local area coverage data at full radiometric and spatial resolution have been collocated with rain-rate data from three Doppler weather radars.

Using an interactive computing environment, large independent samples of cloudy-altocumulus, cumulonimbus, cirrostratus, cumulus, nimbostratus, stratocumulus, stratus-and cloud-free scenes have been identified (labeled) in the collocated data. Accurate labeling was ensured by providing a supervising-analyst access to appropriate diagnostics, including difference and ratio channels, 3.7-µm reflected and emissive components, spectral histograms, Coakley-Bretherton spatial coherence plots, mean, standard deviation, and gray-level difference (GLD) statistics. This analysis yielded 4323 cloud and no-cloud samples at a spatial resolution of 8 × 8 instantaneous fields of view (IFOV), from 257 NOAA-11 and NOAA-12 orbits.

Bayesian cloud discriminant functions calculated from the labeled samples and utilizing feature vectors including radiometric and GLD spatial characteristics successfully classified scenes into one of the seven cloud and no-cloud classes with significant skill (Kuipers’ performance index 0.63). Utilizing the posterior probability of the classified samples enabled some clouds that were classified erroneously to be identified (and discarded), improving the skill of the discriminant functions by an additional 10% or so. Removing the GLD statistics from the feature vector reduced the skill of the cloud discrimination by about 20% (relative to the nondiscarding discriminant function), while increasing the misclassification of midlevel clouds. However, some cloud classes can only be discriminated from their multispectral signatures. Day and night discriminant functions show similar skill.

Within raining cloud classes, rain rate has been related to the spatial and radiometric characteristics of the cloud. The skill of the rain-rate estimates is dependent on the cloud type. For nimbostratus and altocumulus classes 20%–25% of the rain-rate variation can be explained by predictors that measure the temperature, spatial texture, and degree of isotropy in the sampled clouds. Raining and nonraining samples of altocumulus, cumulus, cirrostratus, and nimbostratus can be delineated with at least 60% accuracy.

This approach, whereby cloud classes are identified then rain rates estimated as a function of cloud type, would seem to resolve some of the usual problems associated with rain-rate analyses from midlatitudes infrared and visible satellite data. It also extends rain-rate diagnosis to nonconvective (frontal) cloud systems.

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Yang Yang
,
Michael Uddstrom
,
Grant Pearce
, and
Mike Revell

Abstract

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.

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Michael J. Uddstrom
,
John A. McGregor
,
Warren R. Gray
, and
John W. Kidson

Abstract

This paper reports on the first application of a multispectral textural Bayesian cloud classification algorithm (“SRTex”) to the general problem of the determination of high–spatial resolution cloud-amount and cloud-type climatological distributions. One year of NOAA-14 daylight passes over a region of complex topography (the South Island of New Zealand and adjacent ocean areas) is analyzed, and exploratory cloud-amount and -type climatological distributions are developed. When validated against a set of surface observations, the cloud-amount distributions have no significant bias at seasonal and yearly timescales, and explain between 70% (seasonal) and 90% (annual) of the spatial variance in the surface observations.

The cloud-amount distributions show strong land/sea contrasts. Lowest cloud frequencies are found in the lee of the major alpine feature in the analysis domain (the Southern Alps) and over mountain-sheltered valleys and adjacent sea areas. Over the oceans, cloud frequencies are highest over sub-Antarctic water masses, and range from 90% to 95%. However, over the sea adjacent to the coast on the western side of the Southern Alps, there is a distinct minimum in cloud amount that appears to be related to the orography.

The cloud-type climatological distributions are analyzed in terms of both simple frequency of occurrence and conditional frequency of occurrence, which is the frequency of occurrence as a fraction of the total number of times that the cloud type could have been observed. These distributions reveal the presence of preferred locations for some cloud types. There is strong evidence that uplift over major mountain ranges is a source of transmissive cirrus (enhancing occurrence by a factor of 2) and that the resulting cirrus coverage is most extensive and frequent in spring. Over the ocean areas, SST-related effects may determine the spatial distributions of stratocumulus, with higher frequencies observed over sub-Antarctic waters than over subtropical waters. Also, there is a positive correlation between mean cloud-top height and SST, but no similar relationship is found for other cloud types.

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Richard Turner
,
Xiaogu Zheng
,
Neil Gordon
,
Michael Uddstrom
,
Greg Pearson
,
Rilke de Vos
, and
Stuart Moore

Abstract

Wind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.

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