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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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Elizabeth E. Ebert, John E. Janowiak, and Chris Kidd

An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison.

Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precipitation occurrence, amount, and intensity are most accurate during the warm season and at lower latitudes, where the rainfall is primarily convective in nature. In contrast, the NWP models perform better than the satellite estimates during the cool season when non-convective precipitation is dominant. An optimal rain-monitoring strategy for remote regions might therefore judiciously combine information from both satellite and NWP models.

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Elizabeth E. Ebert, Ulrich Damrath, Werner Wergen, and Michael E. Baldwin

Twenty-four-hour and 48-h quantitative precipitation forecasts (QPFs) from 11 operational numerical weather prediction models have been verified for a 4-yr period against rain gauge observations over the United States, Germany, and Australia to assess their skill in predicting the occurrence and amount of daily precipitation.

Model QPFs had greater skill in winter than in summer, and greater skill in midlatitudes than in Tropics, where they performed only marginally better than “persistence.” The best agreement among models, as well as the best ability to discriminate raining areas, occurred for a low rain threshold of 1–2 mm d−1. In contrast, the skill for forecasts of rain greater than 20 mm d−1 was generally quite low, reflecting the difficulty in predicting precisely when and where heavy rain will fall. The location errors for rain systems, determined using pattern matching with the observations, were typically about 100 km for 24-h forecasts, with smaller errors occurring for the heaviest rain systems.

It does not appear that model QPFs improved significantly during the four years examined. As new model versions were introduced their performance changed, not always for the better. The process of improving model numerics and physics is a complicated juggling act, and unless the accurate prediction of rainfall is made a top priority then improvements in model QPF will continue to come only slowly.

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Judith A. Curry, Julie L. Schramm, and Elizabeth E. Ebert

Abstract

The sea ice-albedo feedback mechanism over the Arctic Ocean multiyear sea ice is investigated by conducting a series of experiments using several one-dimensional models of the coupled sea ice-atmosphere system. In its simplest form, ice-albedo feedback is thought to be associated with a decrease in the areal cover of snow and ice and a corresponding increase in the surface temperature, further decreasing the areal cover of snow and ice. It is shown that the sea ice-albedo feedback can operate even in multiyear pack ice, without the disappearance of this ice, associated with internal processes occurring within the multiyear ice pack (e.g., duration of the snow cover, ice thickness, ice distribution, lead fraction, and melt pond characteristics).

The strength of the ice-albedo feedback mechanism is compared for several different thermodynamic sea ice models: a new model that includes ice thickness distribution, the Ebert and Curry model, the Maykut and Untersteiner model, and the Semtner level-3 and level-0 models. The climate forcing is chosen to be a perturbation of the surface heat flux, and cloud and water vapor feedbacks are inoperative so that the effects of the sea ice-albedo feedback mechanism can be isolated. The inclusion of melt ponds significantly strengthens the ice-albedo feedback, while the ice thickness distribution decreases the strength of the modeled sea ice-albedo feedback. It is emphasized that accurately modeling present-day sea ice thickness is not adequate for a sea ice parameterization; the correct physical processes must be included so that the sea ice parameterization yields correct sensitivities to external forcing.

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David Ahijevych, Eric Gilleland, Barbara G. Brown, and Elizabeth E. Ebert

Abstract

Several spatial forecast verification methods have been developed that are suited for high-resolution precipitation forecasts. They can account for the spatial coherence of precipitation and give credit to a forecast that does not necessarily match the observation at any particular grid point. The methods were grouped into four broad categories (neighborhood, scale separation, features based, and field deformation) for the Spatial Forecast Verification Methods Intercomparison Project (ICP). Participants were asked to apply their new methods to a set of artificial geometric and perturbed forecasts with prescribed errors, and a set of real forecasts of convective precipitation on a 4-km grid. This paper describes the intercomparison test cases, summarizes results from the geometric cases, and presents subjective scores and traditional scores from the real cases.

All the new methods could detect bias error, and the features-based and field deformation methods were also able to diagnose displacement errors of precipitation features. The best approach for capturing errors in aspect ratio was field deformation. When comparing model forecasts with real cases, the traditional verification scores did not agree with the subjective assessment of the forecasts.

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Elizabeth E. Ebert, Ulrich Schumann, and Roland B. Stull

Abstract

Large-eddy simulation is used to simulate quasi-steady state convection in a windless mixed layer over a uniform surface with constant heat flux. Different tracers are injected at each discrete height in the model to track vertical transport of tracers as a function of time. The resulting tracer source and destination information is presented in the form of transilient matrices.

These matrices are asymmetric for time increments on the order of the convective time scale, t *. They show nonlocal mixing occurring over a range of wavelengths up to the mixed layer depth, some convective overturning, and the loss of nearly all of the surface layer air into thermals. Measurements of transport across finite distances exhibit skewed distributions of vertical transport velocity. The relative importance of upward versus downward transport strongly depends on both height and time, as measured by the fractional transport and mixing lengths in each direction. Process, mass, and heat transport spectra show the relatively minor contribution made by small-size eddies as compared to the medium and large scales. Favorable comparisons of these results with a variety of traditional turbulence statistics exemplify the wealth of turbulence information that is captured within a transilient matrix.

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Edwin W. Eloranta, Roland B. Stull, and Elizabeth E. Ebert

Abstract

A calibration device was designed to fit over the Lyman-α (LA) probes on the NCAR King Air aircraft to allow the introduction of pure nitrogen, oxygen, and carbon dioxide gases into the probe's radiation path. With these three gases, it was possible to calculate three of the most important terms in the LA humidity equation: path length, reference voltage (radiation) and oxygen absorption. This calibration device was tested in France during the HAPEX-MOBILHY field program, and was found to perform successfully.

As a result of the calibration, it was found that the effective LA path lengths during HAPEX were significantly different from the “nominal” path length physically set at the start of the experiment. Also, the oxygen absorption cross section was over twice as large as the published values, suggesting that the emission spectra of the lamps used in the LA probes are contaminated with other emission lines. The measured LA probe output reference voltages for no absorption were found to be slowly varying in time, suggesting that inflight “floating” calibrations against another reference hygrometer are necessary, in addition to the pre- and post-flight calibrations on the ground using the test device.

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Eric Gilleland, David Ahijevych, Barbara G. Brown, Barbara Casati, and Elizabeth E. Ebert

Abstract

Advancements in weather forecast models and their enhanced resolution have led to substantially improved and more realistic-appearing forecasts for some variables. However, traditional verification scores often indicate poor performance because of the increased small-scale variability so that the true quality of the forecasts is not always characterized well. As a result, numerous new methods for verifying these forecasts have been proposed. These new methods can mostly be classified into two overall categories: filtering methods and displacement methods. The filtering methods can be further delineated into neighborhood and scale separation, and the displacement methods can be divided into features based and field deformation. Each method gives considerably more information than the traditional scores, but it is not clear which method(s) should be used for which purpose.

A verification methods intercomparison project has been established in order to glean a better understanding of the proposed methods in terms of their various characteristics and to determine what verification questions each method addresses. The study is ongoing, and preliminary qualitative results for the different approaches applied to different situations are described here. In particular, the various methods and their basic characteristics, similarities, and differences are described. In addition, several questions are addressed regarding the application of the methods and the information that they provide. These questions include (i) how the method(s) inform performance at different scales; (ii) how the methods provide information on location errors; (iii) whether the methods provide information on intensity errors and distributions; (iv) whether the methods provide information on structure errors; (v) whether the approaches have the ability to provide information about hits, misses, and false alarms; (vi) whether the methods do anything that is counterintuitive; (vii) whether the methods have selectable parameters and how sensitive the results are to parameter selection; (viii) whether the results can be easily aggregated across multiple cases; (ix) whether the methods can identify timing errors; and (x) whether confidence intervals and hypothesis tests can be readily computed.

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Eric Gilleland, David A. Ahijevych, Barbara G. Brown, and Elizabeth E. Ebert

Numerous new methods have been proposed for using spatial information to better quantify and diagnose forecast performance when forecasts and observations are both available on the same grid. The majority of the new spatial verification methods can be classified into four broad categories (neighborhood, scale separation, features based, and field deformation), which themselves can be further generalized into two categories of filter and displacement. Because the methods make use of spatial information in widely different ways, users may be uncertain about what types of information each provides, and which methods may be most beneficial for particular applications. As an international project, the Spatial Forecast Verification Methods Inter-Comparison Project (ICP; www.ral.ucar.edu/projects/icp) was formed to address these questions. This project was coordinated by NCAR and facilitated by the WMO/World Weather Research Programme (WWRP) Joint Working Group on Forecast Verification Research. An overview of the methods involved in the project is provided here with some initial guidelines about when each of the verification approaches may be most appropriate. Future spatial verification methods may include hybrid methods that combine aspects of filter and displacement approaches.

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Jingru Dai, Michael J. Manton, Steven T. Siems, and Elizabeth E. Ebert

Abstract

Wintertime precipitation in the Snowy Mountains provides water for agriculture, industry, and domestic use in inland southeastern Australia. Unlike most of Australia, much of this precipitation falls as snow, and it is recorded by a private network of heated tipping-bucket gauges. These observations are used in the present study to assess the accuracy of a poor man’s ensemble (PME) prediction of precipitation in the Snowy Mountains based on seven numerical weather prediction models. While the PME performs quite well, there is significant underestimation of precipitation intensity. It is shown that indicators of the synoptic environment can be used to improve the PME estimates of precipitation. Four synoptic regimes associated with different precipitation classes are identified from upper-air data. The reliability of the PME forecasts can be sharpened by considering the precipitation in each of the four synoptic classes. A linear regression, based on the synoptic classification and the PME estimate, is used to reduce the forecast errors. The potential to extend the method for forecasting purposes is discussed.

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