The data-rich International H2O Project (IHOP_2002) experiment is used to study convective storm initiation and subsequent evolution for all days of the experiment. Initiation episodes were almost evenly divided between those triggered along surface-based convergence lines and elevated initiation episodes that showed no associated surface convergence. The elevated episodes occurred mostly at night, and the surface-based episodes occurred during the afternoon and evening. Surface-based initiations were mostly associated with synoptic fronts and gust fronts and less so with drylines and bores. Elevated initiations were frequently associated with observable convergent or confluent features in the Rapid Update Cycle (RUC) wind analysis fields between 900 and 600 hPa. The RUC10 3-h forecast of the precipitation initiation episodes were correct 44% of the time, allowing a tolerance of 250 km in space and for the forecast being early by one period. However, the accuracy was closely tied to the scale of ...

Summary of Convective Storm Initiation and Evolution during IHOP: Observational and Modeling Perspective

This study identifies the precursor signals of convective initiation within sequences of 1-km-resolution visible (VIS) and 4–8-km infrared (IR) imagery from the Geostationary Operational Environmental Satellite (GOES) instrument. Convective initiation (CI) is defined for this study as the first detection of Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivities ≥35 dBZ produced by convective clouds. Results indicate that CI may be forecasted ∼30–45 min in advance through the monitoring of key IR fields for convective clouds. This is made possible by the coincident use of three components of GOES data: 1) a cumulus cloud “mask” at 1-km resolution using VIS and IR data, 2) satellite-derived atmospheric motion vectors (AMVs) for tracking individual cumulus clouds, and 3) IR brightness temperature (TB) and multispectral band-differencing time trends. In effect, these techniques isolate only the cumulus convection in satellite imagery, track moving cumulus convection, and evaluate various IR...

Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery

The evolution of cumulus clouds over a variety of radar-detected, boundary layer convergence features in eastern Colorado has been examined using Geostationary Operational Environmental Satellite (GOES) imagery and Weather Surveillance Radar-1988 Doppler (WSR-88D) data. While convective storms formed above horizontal rolls in the absence of any additional surface forcing, the most intense storms initiated in regions above: gust fronts, gust front interaction with horizontal rolls, and terrain-induced stationary convergence zones. The onset of vigorous cloud growth leading to storm development was characterized by cloud tops that reached subfreezing temperatures and exhibited large cooling rates at cloud top 15 min prior to the first detection of 10dBZ radar echoes aloft and 30 min before 35 dBZ. The rate of cloud-top temperature change was found to be important for discriminating between weakly precipitating storms (,35 dBZ) and vigorous convective storms (.35 dBZ). Results from this study have been used to increase the lead time of thunderstorm initiation nowcasts with the NCAR automated, convective storm nowcasting system. This improvement is demonstrated at two operational forecast offices in Virginia and New Mexico.

https://olympic.atmos.colostate.edu/pdf/NowcastStorms.pdf

Nowcasting Storm Initiation and Growth Using GOES-8 and WSR-88D Data

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility, aerosol/ash loading, ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can also play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in situ instruments at the surface and in the atmosphere, as well as aircraft and Unmanned Aerial Vehicles mounted sensors, are becoming more common. At smaller time and space scales (e.g., < 1 km), meteorological forecasts from NWP models need to be continuously improved for accurate physical parameterizations. Aviation weather forecasts also need to be developed to provide detailed information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.

/pdf/a-review-of-high-impact-weather-for-aviation-meteorology-283dpf7vzd.pdf

A Review of High Impact Weather for Aviation Meteorology

The Auto-Nowcast System (ANC), a software system that produces time- and space-specific, routine (every 5 min) short-term (0‐1 h) nowcasts of storm location, is presented. A primary component of ANC is its ability to identify and characterize boundary layer convergence lines. Boundary layer information is used along with storm and cloud characteristics to augment extrapolation with nowcasts of storm initiation, growth, and dissipation. A fuzzy logic routine is used to combine predictor fields that are based on observations (radar, satellite, sounding, mesonet, and profiler), a numerical boundary layer model and its adjoint, forecaster input, and feature detection algorithms. The ANC methodology is illustrated using nowcasts of storm initiation, growth, and dissipation. Statistical verification shows that ANC is able to routinely improve over extrapolation and persistence.

/pdf/ncar-auto-nowcast-system-du8hrnnvkz.pdf

J. Yee

Papers

NCAR Auto-Nowcast System