The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere. Advanced spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved cloud and moisture imagery with the 16-channel Advanced Baseline Imager (ABI). The GLM will map total lightning activity continuously day and night with near-uniform storm-scale spatial resolution of 8 km with a product refresh rate of less than 20 s over the Americas and adjacent oceanic regions in the western hemisphere. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low Earth orbit, and from ground-based lightning networks and intensive prelaunch field campaigns. The GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extend their combined climatology over the western hemisphere into the coming decades. Science and application development along with preoperational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and checkout of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

The GOES-R Geostationary Lightning Mapper (GLM)

Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models,the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of forecasts become available.

Forecasting lightning threat using cloud- resolving model simulations

The Geostationary Lightning Mapper (GLM) is a new geostationary lightning detection and location instrument, developed for the current generation of Geostationary Operational Environmental Satellite-R Series (GOES-R, S, T, U) satellites. Detection efficiency (DE) and false alarm rate (FAR) are evaluations of what an instrument detects and what it misses, compared with other well-characterized measurements. These are critical parameters to be assessed for the GLM. We now have enough data to produce preliminary DE and FAR assessments of the prelaunch predicted DE and FAR performance specifications for GLM. However, one of the challenges of doing this assessment is that all available ground truth data sets have much smaller coverage, and in some places, lower DE than GLM. In an attempt to overcome these limitations, we have created a “virtual” lightning network by clustering flashes from several different ground-based networks, using a technique similar to the clustering of GLM pixels. All ground truth sources that are within 330 ms and 16.5 km of any other ground truth source are added to the current cluster. Treating this as a single, “virtual” network, we calculated the relative DE and FAR of GLM. We found that the GLM easily meets the DE spec of better than 70% averaged across the whole field-of-view, over 24 h. It meets the FAR spec in most of CONUS, and we suspect it will improve with updated processing software.

Preliminary detection efficiency and false alarm rate assessment of the Geostationary Lightning Mapper on the GOES-16 satellite

The ability of the High-Resolution Rapid Refresh (HRRR) model to forecast the location of convective storms is of interest for a variety of applications. Since lightning is often present wi...

/pdf/comparison-of-lightning-forecasts-from-the-high-resolution-3korns62wl.pdf

Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations


The High-Resolution Ensemble Forecast system (HREF) Calibrated Thunder guidance is a suite of probabilistic forecast products designed to predict the likelihood of at least one cloud-to-ground (CG) lightning flash within 20 km (12 miles) of a point during a given 1-hour, 4-hour, and 24-hour time interval. This guidance takes advantage of a combination of storm-attribute and environmental fields produced by the convection-allowing HREF to objectively improve upon lightning forecasts generated by the non-convection-allowing Short-Range Ensemble Forecast system (SREF). This study provides an overview of how the HREF Calibrated Thunder guidance was developed and calibrated to be statistically reliable against observed CG lightning flashes recorded by the National Lightning Detection Network (NLDN). Performance metrics for the 1-hour, 4-hour, and 24-hour guidance are provided and compared to the respective SREF calibrated probabilistic lightning forecasts. The HREF Calibrated Thunder guidance has been implemented operationally within the National Weather Service and is now available to the public.

Utilizing the High-Resolution Ensemble Forecast System to Produce Calibrated Probabilistic Thunderstorm Guidance

Localized Aviation MOS Program (LAMP) convection and lightning probability and “potential” guidance forecasts for the conterminous United States, developed by the Meteorological Development...

LAMP Upgraded Convection and Total Lightning Probability and “Potential” Guidance for the Conterminous United States

A heavy dust storm originating in Mongolia and Inner Mongolia traveled to Northeast China and met a midlatitude frontal system on May 3, 2017. The potential ice nuclei (IN) effects of mineral dust aerosols on the vertical structure of clouds, precipitation, and latent heat (LH) were studied using Global Precipitation Mission (GPM) satellite observations and Weather Research and Forecasting (WRF) model simulations. The WRF simulations correctly captured the main features of the system, and the surface rain rate distribution was positively correlated with data retrieved from the GPM Microwave Imager. Moreover, the correlation coefficient increased from 0.31 to 0.54 with increasing moving average window size. The WRF-simulated rainfall vertical profiles are generally comparable to the GPM Dual-Frequency Precipitation Radar (DPR) observations, particularly in low layers. The joint probability distribution functions of the rain rate at different altitudes from the WRF simulation and GPM observations show high positive correlation coefficients of ~0.80, indicating that the assumptions regarding the raindrop size distribution in the WRF model and DPR retrieval were consistent. Atmospheric circulation analysis and aerosol optical depth observations from the Himawari-8 satellite indicated that the dust storm entered only a narrow strip of the northwest edge of the frontal precipitation system. The WRF simulations showed that in carefully selected areas of heavy dust, dust can enhance the heterogeneous ice nucleation process and increase the cloud ice, snowfall, high-altitude precipitation rate, and LH rate in the upper layers. This effect is significant at temperatures of −15 °C to −38 °C and requires dust number concentrations exceeding 106 m−3. It is important to accurately classify the dusty region in this type of case study. In the selected vertical cross section, the WRF-simulated and DPR-retrieved LH have comparable vertical shapes and amplitudes. Both results reflect the structure of the tilted frontal surface, with positive LH above it and negative LH below it. The simulated area-averaged LH profiles show positive heating in the entire column, which is a convective-dominated region, and this feature is not significantly affected by dust. DPR-based LH profiles show stratiform-dominated or convective-dominated shapes, depending on the DPR retrieval product.

Characteristics of clouds, precipitation, and latent heat in midlatitude frontal system mixed with dust storm from GPM satellite observations and WRF simulations

Abstract. The potential impacts of dust aerosols and atmospheric convective
available potential energy (CAPE) on the vertical development of
precipitating clouds in southeastern China (20–30∘ N, 110–125∘ E) in June, July, and August from 2000 to
2013 were studied using multisource observations. In southeastern
China, heavy-dust conditions are coupled with strong northerly winds that
transport air masses containing high concentrations of mineral dust particles, with
cold temperatures, and with strong wind shear. This leads to weaker CAPE on dusty
days compared with that on pristine days. Based on satellite observations,
precipitating drops under dusty conditions grow faster in the middle atmospheric layers
(with a temperature of between −5 and +2 ∘C) but slower in the
upper and lower layers compared with their pristine counterparts. For a given
precipitation top height (PTH), the precipitation rate under dusty
conditions is lower in the upper layer but higher in the middle and lower layers.
Moreover, the associated latent heating rate released by precipitation in the middle
layer is higher. The precipitation top temperature (PTT) shows a fairly good
linear relationship with the near-surface rain rate (NSRR): the linear
regression slope between the PTT and NSRR is stable under dusty and pristine
conditions. However, the PTT0 (the PTT related to rain onset) at the onset
of precipitation is highly affected by both the CAPE and aerosol conditions. On pristine
days, a stronger CAPE facilitates the vertical development of precipitation
and leads to a decrease in PTT0, at a rate of −0.65 ∘C per 100 J kg−1 of CAPE for deep convective precipitation (with a variation of 15 %) and at a rate of −0.41 ∘C per 100 J kg−1 of CAPE for stratiform
precipitation (with variation of 12 %). After removing the impacts of CAPE
on PTT, dust aerosols led to an increase in PTT0, at a rate of +4.19 ∘C per unit aerosol optical depth (AOD) for deep convective
precipitation and at a rate of +0.35 ∘C per unit AOD for stratiform
precipitation. This study showed clear evidence that meteorological conditions
and aerosol conditions combine to impact the vertical
development of precipitation clouds. A quantitative estimation of the
sensitivity of PTT to CAPE and dust was also provided.


/pdf/the-impacts-of-dust-aerosol-and-convective-available-316ap3mp.pdf

The impacts of dust aerosol and convective available potential energy on precipitation vertical structure in southeastern China as seen from multisource observations

Chenjie Huang

Papers

LAMP Upgraded Convection and Total Lightning Probability and “Potential” Guidance for the Conterminous United States

Characteristics of clouds, precipitation, and latent heat in midlatitude frontal system mixed with dust storm from GPM satellite observations and WRF simulations

The impacts of dust aerosol and convective available potential energy on precipitation vertical structure in southeastern China as seen from multisource observations