Abstract

In this paper, we extend the 1985 work of Kuo and Anthes to develop a method to derive temperature and geopotential information from a network of wind profiler observations, which we define as a thermodynamic retrieval technique. We first reformulate the retrieval procedure on a terrain-following σ-coordinate, similar to that used in most limited-area models. We then examine the accuracy of the derived temperature and geopotential fields for six different synoptic situations using basic datasets generated by a high-resolution mesoscale model.

We found that in the middle and upper troposphere, the retrieved temperature is significantly more accurate than the direct temperature measurement from a combined satellite- and ground-based microwave radiometric system (using climatology as a basis for radiometric retrieval). At lower levels, however, the retrieved temperature is not as accurate, mainly due to the neglect of boundary layer momentum fluxes in the retrieval calculations. Because the boundary layer momentum fluxes are difficult to measure, this suggests a need for independent temperature measurements at these levels.

Formulating the retrieval procedure in either the p- or the σ-coordinate system gives similar results, with the retrieved temperature slightly more accurate in the σ-coordinate than in the p-coordinate formulation. The use of Neumann boundary conditions, with no independent temperature observations at the lateral boundaries, results in a 61% larger error than does the use of the Dirichlet boundary conditions when independent temperature observations, such as those from the rawinsonde systems, are available. A modified specification of the Dirichlet boundary condition is developed which utilizes an arbitrary number of temperature sounders in combination with the horizontal geopotential gradient estimated from the wind field. It is shown that better results are obtained when more temperature sounders are available at the boundaries. It is also shown that the utilization of the geopotential gradient, obtained from the wind field, reduces the number of independent sounders required at the boundaries.

The accuracy of the retrieved temperature is case-dependent. The retrieval procedure is shown to be more accurate in cases with weak dynamical forcing. When there is strong baroclinicity, vertical motion and divergence, the retrieved temperatures exhibit a larger error. This is caused, in part, by a lack of horizontal resolution of the hypothetical wind profiler network needed to resolve strong mesoscale circulations near the frontal zone, and partially by large errors produced in the calculation of divergence and vertical motion terms over these regions. To obtain a better assessment of the accuracy of the retrieved temperature, we define a parameter—the retrieval ratio—as the ratio of the rms temperature error of the retrieved temperature to that of a rawinsonde network which has the same horizontal resolution as the profiler network. We found that in a weak summertime case, the retrieval ratio is 1.0, indicating that the retrieved temperature is as accurate as that from a rawinsonde network. In cases with strong dynamical forcing, the retrieval ratio is about 1.4, indicating less accuracy in these situations. The mean for the six cases studied here is 1.25, showing that the retrieved temperature, on the average, contains 25% higher rms error than the simulated rawinsonde temperature. When the balance equation is used instead of the divergence equation in the retrieval calculation, the mean retrieval ratio for all six synoptic events is near 1.1.

The neglect of the local tendency of divergence, or terms related to divergence, produces considerable degradation of the retrieved temperature in an experiment with high-resolution perfect wind data. This implies that the local tendency term and the divergence terms are important for small mesoscale systems (those that can be resolved by a 40 km model). However, ignoring the divergence related terms in the divergence equation produces improved results in other experiments, when the profiler data are available at 360 km intervals with superimposed measurement errors. This is partially due to the fact that the divergence terms are less important for larger-scale systems, and partially because the calculation of these terms is very sensitive to measurement and analysis errors.

Abstract

The Weather Research and Forecasting (WRF) Model is a numerical weather prediction model supported by the National Center for Atmospheric Research (NCAR) to a worldwide community of users. In recognition of the growing use of cloud computing, NCAR is now supporting the model in cloud environments. Specifically, NCAR has established WRF setups with select cloud service providers and produced documentation and tutorials on running WRF in the cloud. Described here are considerations in WRF cloud use and the supported resources, which include cloud setups for the WRF system and a cloud-based tool for model code testing.

Abstract

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

Abstract

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.