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Elizabeth E. Ebert

Abstract

High-resolution forecasts may be quite useful even when they do not match the observations exactly. Neighborhood verification is a strategy for evaluating the “closeness” of the forecast to the observations within space–time neighborhoods rather than at the grid scale. Various properties of the forecast within a neighborhood can be assessed for similarity to the observations, including the mean value, fractional coverage, occurrence of a forecast event sufficiently near an observed event, and so on. By varying the sizes of the neighborhoods, it is possible to determine the scales for which the forecast has sufficient skill for a particular application. Several neighborhood verification methods have been proposed in the literature in the last decade. This paper examines four such methods in detail for idealized and real high-resolution precipitation forecasts, highlighting what can be learned from each of the methods. When applied to idealized and real precipitation forecasts from the Spatial Verification Methods Intercomparison Project, all four methods showed improved forecast performance for neighborhood sizes larger than grid scale, with the optimal scale for each method varying as a function of rainfall intensity.

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Elizabeth E. Ebert
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Elizabeth E. Ebert

Abstract

A poor man's ensemble is a set of independent numerical weather prediction (NWP) model forecasts from several operational centers. Because it samples uncertainties in both the initial conditions and model formulation through the variation of input data, analysis, and forecast methodologies of its component members, it is less prone to systematic biases and errors that cause underdispersive behavior in single-model ensemble prediction systems (EPSs). It is also essentially cost-free. Its main disadvantage is its relatively small size. This paper investigates the ability of a poor man's ensemble to provide forecasts of the probability and distribution of rainfall in the short range, 1–2 days.

The poor man's ensemble described here consists of 24- and 48-h daily quantitative precipitation forecasts (QPFs) from seven operational NWP models. The ensemble forecasts were verified for a 28-month period over Australia using gridded daily rain gauge analyses. Forecasts of the probability of precipitation (POP) were skillful for rain rates up to 50 mm day−1 for the first 24-h period, exceeding the skill of the European Centre for Medium-Range Weather Forecasts EPS. Probabilistic skill was limited to lower rain rates during the second 24 h. The skill and accuracy of the ensemble mean QPF far exceeded that of the individual models for both forecast periods when standard measures such as the root-mean-square error and equitable threat score were used. Additional measures based on the forecast location and intensity of individual rain events substantiated the improvements associated with the ensemble mean QPF. The greatest improvement was seen in the location of the forecast rain pattern, as the mean displacement from the observations was reduced by 30%. As a result the number of event forecasts that could be considered “hits” (forecast rain location and maximum intensity close to the observed) improved markedly.

Averaging to produce the ensemble mean caused a large bias in rain area and a corresponding reduction in mean and maximum rain intensity. Several alternative deterministic ensemble forecasts were tested, with the most successful using probability matching to reassign the ensemble mean rain rates using the rain rate distribution of the component QPFs. This eliminated most of the excess rain area and increased the maximum rain rates, improving the event hit rate.

The dependence of the POP and ensemble mean results on the number of members included in the ensemble was investigated using the 24-h model QPFs. When ensemble members were selected randomly the performance improved monotonically with increasing ensemble size, with verification statistics approaching their asymptotic limits for an ensemble size of seven. When the members were chosen according to greatest overall skill the ensemble performance peaked when only five or six members were used. This suggests that the addition of ensemble members with lower skill can degrade the overall product. Low values of the spread–skill correlation indicate that it is not possible to predict the forecast skill from the spread of the ensemble alone. However, the number of models predicting a particular rain event gives a good indication of the likelihood of the ensemble to envelop the location and magnitude of that event.

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Elizabeth E. Ebert

Abstract

The analysis of cloud cover in the polar regions from satellite data is more difficult than at other latitudes because the visible and thermal contrasts between the cloud cover and the underlying surface are frequently quite small. Pattern recognition has proven to be a useful tool in detecting and identifying several cloud types over snow and ice. Here a pattern recognition algorithm in combined with a hybrid histogram-spatial coherence (HHSC) scheme to derive cloud classification and fractional coverage, surface and cloud visible albedos and infrared brightness temperatures from multispectral AVHRR satellite imagery. The accuracy of the cloud fraction estimates were between 0.05 and 0.26, based on the mean absolute difference between the automated and manual nephanalyses of nearly 1000 training samples. The HHSC demonstrated greater accuracy at estimating cloud friction than three different threshold. methods. An important result is that the prior classification of a sample may significantly improve the accuracy of the analysis of cloud fraction, albedos and brightness temperatures over that of an unclassified sample.

The algorithm is demonstrated for a set of AVHRR imagery from the summertime Arctic. The automated classification and analysis are in good agreement with manual interpretation of the satellite imagery and with surface observations.

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Chermelle Engel and Elizabeth E. Ebert

Abstract

This paper describes an extension of an operational consensus forecasting (OCF) scheme from site forecasts to gridded forecasts. OCF is a multimodel consensus scheme including bias correction and weighting. Bias correction and weighting are done on a scale common to almost all multimodel inputs (1.25°), which are then downscaled using a statistical approach to an approximately 5-km-resolution grid. Local and international numerical weather prediction model inputs are found to have coarse scale biases that respond to simple bias correction, with the weighted average consensus at 1.25° outperforming all models at that scale. Statistical downscaling is found to remove the systematic representativeness error when downscaling from 1.25° to 5 km, though it cannot resolve scale differences associated with transient small-scale weather.

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Elizabeth E. Ebert and Gary T. Weymouth

Abstract

Geostationary satellite observations can be used to distinguish potential rain-bearing clouds from nonraining areas, thereby providing surrogate observations of “no rain” over large areas. The advantages of including such observations are the provision of data in regions void of conventional rain gauges or radars, as well as the improved delineation of raining from nonraining areas in gridded rainfall analyses.

This paper describes a threshold algorithm for delineating nonraining areas using the difference between the daily minimum infrared brightness temperature and the climatological minimum surface temperature. Using a fixed difference threshold of −13 K, the accuracy of “no rain” detection (defined as the percentage of no-rain diagnoses that was correct) was 98%. The average spatial coverage was 45%, capturing about half of the observed space–time frequency of no rain over Australia. By delineating cool, moderate, and warm threshold areas, the average spatial coverage was increased to 54% while maintaining the same level of accuracy.

The satellite no-rain observations were sampled to a density consistent with the existing gauge network, then added to the real-time gauge observations and analyzed using the Bureau of Meteorology’s operational three-pass Barnes objective rainfall analysis scheme. When verified against independent surface rainfall observations, the mean bias in the satellite-augmented analyses was roughly half of bias in the gauge-only analyses. The most noticeable impact of the additional satellite observations was a 66% reduction in the size of the data-void regions.

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Elizabeth E. Ebert and William A. Gallus Jr.

Abstract

The contiguous rain area (CRA) method for spatial forecast verification is a features-based approach that evaluates the properties of forecast rain systems, namely, their location, size, intensity, and finescale pattern. It is one of many recently developed spatial verification approaches that are being evaluated as part of a Spatial Forecast Verification Methods Intercomparison Project. To better understand the strengths and weaknesses of the CRA method, it has been tested here on a set of idealized geometric and perturbed forecasts with known errors, as well as nine precipitation forecasts from three high-resolution numerical weather prediction models.

The CRA method was able to identify the known errors for the geometric forecasts, but only after a modification was introduced to allow nonoverlapping forecast and observed features to be matched. For the perturbed cases in which a radar rain field was spatially translated and amplified to simulate forecast errors, the CRA method also reproduced the known errors except when a high-intensity threshold was used to define the CRA (≥10 mm h−1) and a large translation error was imposed (>200 km). The decomposition of total error into displacement, volume, and pattern components reflected the source of the error almost all of the time when a mean squared error formulation was used, but not necessarily when a correlation-based formulation was used.

When applied to real forecasts, the CRA method gave similar results when either best-fit criteria, minimization of the mean squared error, or maximization of the correlation coefficient, was chosen for matching forecast and observed features. The diagnosed displacement error was somewhat sensitive to the choice of search distance. Of the many diagnostics produced by this method, the errors in the mean and peak rain rate between the forecast and observed features showed the best correspondence with subjective evaluations of the forecasts, while the spatial correlation coefficient (after matching) did not reflect the subjective judgments.

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Elizabeth E. Ebert and Greg J. Holland

Abstract

A detailed analysis is made of the development of a region of cold cloud-top temperatures in Tropical Cyclone Hilda (1990) in the Coral Sea off eastern Australia. Observed temperatures of approximately 173 K (−100°C) from two independent satellite sources indicate that the convective turrets penetrated well into the stratosphere to an estimated height of around 19.2 km.

The analytical parcel model of Schlesinger is used, together with available observations from the cyclone vicinity, to estimate the convective updrafts required to produce the observed stratosphere penetration. Under realistic assumptions of entrainment and hydrometeor drag, an updraft speed of between 15 and 38 m s−1 at tropopause level is required to provide the observed stratospheric penetration. Independent calculations using observed anvil expansion and environmental CAPE (convective available potential energy) support these updraft findings.

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Elizabeth E. Ebert and Michael J. Manton

Abstract

Over 50 satellite rainfall algorithms were evaluated for a 5° square region in the equatorial western Pacific Ocean during TOGA COARE, November 1992–February 1993. These satellite algorithms used GMS VIS/IR, AVHRR, and SSM/I data to estimate rainfall on both instantaneous and monthly timescales. Validation data came from two calibrated shipboard Doppler radars measuring rainfall every 10 min.

There was large variation among algorithms in the magnitude of the satellite-estimated rainfall, but the patterns of rainfall were similar among algorithm types. Compared to the radar observations, most of the satellite algorithms overestimated the amount of rain falling in the region, typically by about 30%. Patterns of monthly observed rainfall were well represented by the satellite algorithms, with correlation coefficients with the observations ranging from 0.86 to 0.90 for algorithms using geostationary data and 0.69 to 0.86 for AVHRR and SSM/I algorithms when validated on a 0.5° grid. Patterns of instantaneous rain rates were also well analyzed, with correlation coefficients with the radar observations of 0.43–0.58 for the geostationary algorithms and 0.60–0.78 for SSM/I algorithms.

Two case studies are presented to demonstrate the capability of one IR algorithm and three microwave algorithms to estimate instantaneous rainfall rates in the Tropics. The three microwave algorithms differed in their estimates of rain area but all showed greater ability than the IR algorithm to reproduce the spatial pattern of rainfall.

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John L. McBride and Elizabeth E. Ebert

Abstract

Real-time gridded 24-h quantitative precipitation forecasts from seven operational NWP models are verified over the Australian continent. All forecasts have been mapped to a 1° latitude–longitude grid and have been verified against an operational daily rainfall analysis, mapped to the same grid. The verification focuses on two large subregions: the northern tropical monsoon regime and the southeastern subtropical regime. Statistics are presented of the bias score, probability of detection, and false alarm ratio for a range of rainfall threshold values. The basic measure of skill used in this study, however, is the Hanssen and Kuipers (HK) score and its two components: accuracy for events and accuracy for nonevents.

For both regimes the operational models tend to overestimate rainfall in summer and to underestimate it in winter. In the southeastern region the models have HK scores ranging from 0.5 to 0.7, and easily outperform a forecast of persistence. Thus for the current operational NWP models, the 24-h rain forecasts can be considered quite skillful in the subtropics. On the other hand, model skill is quite low in the northern regime with HK values of only 0.2–0.6. During the summer wet season the low skill is associated with an inability to simulate the behavior of tropical convective rain systems. During the winter dry season, it is associated with a low probability of detection for the occasional rainfall event. Thus it could be said that models have no real skill at rainfall forecasts in this monsoonal wet season regime.

Model skill falls dramatically for occurrence thresholds greater than 10 mm day−1. This implies that the models are much better at predicting the occurrence of rain than they are at predicting the magnitude and location of the peak values.

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