Abstract

A new three-dimensional single-Doppler velocity retrieval is introduced and tested with reflectivity and radial velocity data gathered during the Phoenix II field program near Boulder, Colorado. This retrieval is based on reflectivity conservation (with provision for raindrop fall speed), the incompressibility condition, and a temporal constraint on the velocity field. Two temporal constraints are considered: velocity stationarity and Taylor's frozen turbulence approximation (velocity stationarity in a moving reference frame). It is demonstrated that either of these temporal constraints can be used to derive a second conserved scalar from the original conserved scalar (reflectivity). This two-scalar system is, in general, overdetermined, but a least-squares formulation reduces the problem to a Poisson equation for a pseudostreamfunction. The requisite boundary conditions arise naturally from the least-squares formulation and require only single-Doppler measurements for their evaluation.

The new retrieval is tested with Phoenix II data for an optically clear convective planetary boundary layer (22 June 1984) and for a moderate reflectivity microburst (31 May 1984). The input data consist of three consecutive volume scans of radial velocity and reflectivity data from an X-band Doppler radar (NOAA radar C). To validate the retrievals, the retrieved winds are projected into the direction of a second Doppler radar (NOAA radar D) and a direct comparison made with the observed radial winds of that second radar. Experiments focus on data smoothing, optimal time constraints, and raindrop terminal velocity parameterizations. For an optimal treatment of these processes, good agreement is found between the observed and retrieved wind components in both datasets.

Abstract

This study introduces a novel tree-ring dataset, with unparalleled spatial density, for use as a climate proxy. Ancillary Douglas fir and piñon pine tree-ring data collected by the U.S. Forest Service Forest Inventory and Analysis Program (FIA data) were subjected to a series of tests to determine their feasibility as climate proxies. First, temporal coherence between the FIA data and previously published tree-ring chronologies was found to be significant. Second, spatial and temporal coherence between the FIA data and water year precipitation was strong. Third, the FIA data captured the El Niño–Southern Oscillation dipole and revealed considerable latitudinal fluctuation over the past three centuries. Finally, the FIA data confirmed the quadrature-phase coupling between wet/dry cycles and Pacific decadal variability known to exist for the Intermountain West. The results highlight the possibility of further developing high-spatial-resolution climate proxy datasets for the western United States. (The preliminary FIA data are provided online at http://cliserv.jql.usu.edu/FIAdata/ in both station and gridded format.)

Abstract

In 2015 the U.S. Department of Energy (DOE) initiated a 4-yr study, the Second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part because of inherent assumptions underlying their boundary layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration (NOAA), and the DOE’s national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote sensing and in situ measurement systems. The study area comprised the Columbia basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailed BAMS papers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

Abstract

Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPG use it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble’s members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere’s future state at DPG’s observing sites. Army meteorologists view E-4DWX’s output via customized figures posted to a restricted website. Some of these figures summarize collective results—for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped—for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

Abstract

The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.