Abstract

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), both operational at NOAA’s National Centers for Environmental Prediction (NCEP) use the Thompson et al. mixed-phase bulk cloud microphysics scheme. This scheme permits predicted surface precipitation to simultaneously consist of rain, snow, and graupel at the same location under certain conditions. Here, the explicit precipitation-type diagnostic method is described as used in conjunction with the Thompson et al. scheme in the RAP and HRRR models. The postprocessing logic combines the explicitly predicted multispecies hydrometeor data and other information from the model forecasts to produce fields of surface precipitation type that distinguish between rain and freezing rain, and to also portray areas of mixed precipitation. This explicit precipitation-type diagnostic method is used with the NOAA operational RAP and HRRR models. Verification from two winter seasons from 2013 to 2015 is provided against METAR surface observations. An example of this product from a January 2015 south-central United States winter storm is also shown.

Abstract

Stormscale Operational and Research Meteorology-Fronts Experimental Systems Test (STORM-FEST) was held from 1 February to 15 March 1992 in the central United States as a preliminary field systems test for an eventual larger-scale program. One of the systems tested was a remote operations center, located in Boulder, Colorado, which was significantly displaced from the main field concentration of scientists and research aircraft. In concert with the remote operations center test was a test of remote forecasting support, also centered in Boulder. The remote forecasting for STORM-FEST was the first major cooperative effort for the Boulder-Denver Experimental Forecast Facility (EFF), a cooperative effort between operations and research aimed at finding more effective ways of addressing applied meteorological problems. Two other newly formed EFF's, at Norman, Oklahoma, and Kansas City, Missouri, also played key roles in the forecasting/nowcasting support. A description of the design and function of this remote forecasting and nowcasting support is given, followed by an assessment of its utility during STORM-FEST. Although remote forecasting support was deemed plausible based on the STORM-FEST experience, a number of suggestions are given for a more effective way to conduct forecasting experiments and provide forecasting support during a field program.

Abstract

The satellite-based Dvorak technique (DVKT) is the most widely available and readily used tool for operationally estimating the maximum wind speeds associated with tropical cyclones. The DVKT itself produces internally consistent results, is reproducible, and has shown practical accuracy given the high cost of in situ or airborne observations. For these reasons, the DVKT has been used in a reasonably uniform manner globally for approximately 20 years. Despite the nearly universal use of this technique, relatively few systematic verifications of the DVKT have been conducted. This study, which makes use of 20 yr of subjectively determined DVKT-based intensity estimates and best-track intensity estimates influenced by aircraft observations (i.e., ±2 h) in the Atlantic basin, seeks to 1) identify the factors (intensity, intensity trends, radius of outer closed isobar, storm speed, and latitude) that bias the DVKT-based intensity estimates, 2) quantify those biases as well as the general error characteristics associated with this technique, and 3) provide guidance for better use of the operational DVKT intensity estimates. Results show that the biases associated with the DVKT-based intensity estimates are a function of intensity (i.e., maximum sustained wind speed), 12-h intensity trend, latitude, and translation speed and size measured by the radius of the outer closed isobar. Root-mean-square errors (RMSE), however, are shown to be primarily a function of intensity, with the best signal-to-noise (intensity to RMSE) ratio occurring in an intensity range of 90–125 kt (46–64 m s⁻¹). The knowledge of how these factors affect intensity estimates, which is quantified in this paper, can be used to better calibrate Dvorak intensity estimates for tropical cyclone forecast operations, postseason best-track analysis, and climatological reanalysis efforts. As a demonstration of this capability, the bias corrections developed in the Atlantic basin are also tested using a limited east Pacific basin sample, showing that biases and errors can be significantly reduced.

Abstract

An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images.

The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric giving information about location, shape, and size differences of individual features, which could be employed for other spatial forecast verification methods.

Abstract

To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA’s Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED’s potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts.

Abstract

This study uses a series of numerical simulations to examine the structure of the wake of the Hawaiian island of Kauai. The primary focus is on the conditions on 26 June 2003, which was the day of the demise of the Helios aircraft within Kauai’s wake. The simulations show that, in an east-northeasterly trade wind flow, Kauai produces a well-defined wake that can extend 40 km downstream of the island. The wake is bounded to the north and south by regions of strong vertical and horizontal shear—that is, shear lines. These shear lines mark the edge of the wake in the horizontal plane and are aligned approximately parallel to the upstream flow direction at each respective height. The highest-resolution simulations show that these shear lines can become unstable and break down through Kelvin–Helmholtz instability. The breakdown generates turbulent eddies that are advected both downstream and into the recirculating wake flow. Turbulence statistics are estimated from the simulation using a technique that analyzes model-derived structure functions. A number of sensitivity studies are also completed to determine the influence of the upstream conditions on the structure of the wake. These simulations show that directional shear controls the tilt of the wake in the north–south plane with height. These simulations also show that at lower incident wind speeds the wake has a qualitatively similar structure but is less turbulent. At higher wind speeds, the flow regime changes, strong gravity waves are generated, and the wake is poorly defined. These results are consistent with previous idealized studies of stratified flow over isolated obstacles.

Abstract

Clouds over the Southern Ocean are often poorly represented by climate models, but they make a significant contribution to the top-of-atmosphere (TOA) radiation balance, particularly in the shortwave portion of the energy spectrum. This study seeks to better quantify the organization and structure of Southern Hemisphere midlatitude clouds by combining measurements from active and passive satellite-based datasets. Geostationary and polar-orbiter satellite data from the International Satellite Cloud Climatology Project (ISCCP) are used to quantify large-scale, recurring modes of cloudiness, and active observations from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to examine vertical structure, radiative heating rates, and precipitation associated with these clouds. It is found that cloud systems are organized into eight distinct regimes and that ISCCP overestimates the midlevel cloudiness of these regimes. All regimes contain a relatively high occurrence of low cloud, with 79% of all cloud layers observed having tops below 3 km, but multiple-layered clouds systems are present in approximately 34% of observed cloud profiles. The spatial distribution of regimes varies according to season, with cloud systems being geometrically thicker, on average, during the austral winter. Those regimes found to be most closely associated with midlatitude cyclones produce precipitation the most frequently, although drizzle is extremely common in low-cloud regimes. The regimes associated with cyclones have the highest in-regime shortwave cloud radiative effect at the TOA, but the low-cloud regimes, by virtue of their high frequency of occurrence over the oceans, dominate both TOA and surface shortwave effects in this region as a whole.

Abstract

A mesoscale atmospheric forecast model configured in a hybrid isentropic–sigma vertical coordinate and used in the NOAA Rapid Update Cycle (RUC) for operational numerical guidance is presented. The RUC model is the only quasi-isentropic forecast model running operationally in the world and is distinguished from other hybrid isentropic models by its application at fairly high horizontal resolution (10–20 km) and a generalized vertical coordinate formulation that allows model levels to remain continuous and yet be purely isentropic well into the middle and even lower troposphere.

The RUC model is fully described in its 2003 operational version, including numerics and physical parameterizations. The use of these parameterizations, including mixed-phase cloud microphysics and an ensemble-closure-based cumulus parameterization, is fully consistent with the RUC vertical coordinate without any loss of generality.

A series of experiments confirm that the RUC hybrid θ–σ coordinate reduces cross-coordinate transport over a quasi-horizontal σ coordinate. This reduction in cross-coordinate vertical transport results in less numerical vertical diffusion and thereby improves numerical accuracy for moist reversible processes.

Finally, a forecast is presented of a strong cyclogenesis case over the eastern United States in which the RUC model produced an accurate 36-h prediction, especially in a 10-km nested version. Horizontal and vertical plots from these forecasts give evidence of detailed yet coherent structures of potential vorticity, moisture, and vertical motion.

Abstract

Because of limitations of variational and ensemble data assimilation schemes, resulting analysis fields exhibit some noise from imbalance in subsequent model forecasts. Controlling finescale noise is desirable in the NOAA’s Rapid Refresh (RAP) assimilation/forecast system, which uses an hourly data assimilation cycle. Hence, a digital filter initialization (DFI) capability has been introduced into the Weather Research and Forecasting Model and applied operationally in the RAP, for which hourly intermittent assimilation makes DFI essential. A brief overview of the DFI approach, its implementation, and some of its advantages are discussed. Results from a 1-week impact test with and without DFI demonstrate that DFI is effective at reducing high-frequency noise in short-term operational forecasts as well as providing evidence of reduced errors in the 1-h mass and momentum fields. However, DFI is also shown to reduce the strength of parameterized deep moist convection during the first hour of the forecast.

Abstract

The land surface model (LSM) described in this manuscript was originally developed as part of the NOAA Rapid Update Cycle (RUC) model development effort; with ongoing modifications, it is now used as an option for the WRF community model. The RUC model and its WRF-based NOAA successor, the Rapid Refresh (RAP), are hourly updated and have an emphasis on short-range, near-surface forecasts including aviation-impact variables and preconvective environment. Therefore, coupling to this LSM (hereafter the RUC LSM) has been critical to provide more accurate lower boundary conditions. This paper describes changes made to the RUC LSM since earlier descriptions, including extension from six to nine levels, improved snow treatment, and new land-use data from MODIS.

The RUC LSM became operational at the NOAA/National Centers for Environmental Prediction (NCEP) as part of the RUC from 1998–2012 and as part of the RAP from 2012 through the present. The simple treatments of basic land surface processes in the RUC LSM have proven to be physically robust and capable of realistically representing the evolution of soil moisture, soil temperature, and snow in cycled models. Extension of the RAP domain to encompass all of North America and adjacent high-latitude ocean areas necessitated further development of the RUC LSM for application in the tundra permafrost regions and over Arctic sea ice. Other modifications include refinements in the snow model and a more accurate specification of albedo, roughness length, and other surface properties. These recent modifications in the RUC LSM are described and evaluated in this paper.