Search Results

You are looking at 1 - 10 of 15 items for

  • Author or Editor: Justin M. Sieglaff x
  • Refine by Access: All Content x
Clear All Modify Search
Sarah A. Monette and Justin M. Sieglaff

Abstract

The probability of turbulence in the region of identified cloud-top cooling (CTC) from a satellite-based algorithm is calculated. It is found that the overall turbulence probability is low, with only 3.93% of 738 Boeing 737s and 757s experiencing light or greater turbulence. Predicting the probability of turbulence is done using a Bayesian scheme. This prediction scheme relies on the CTC magnitude as well as the relationship between the CTC and aircraft. At higher CTC magnitudes [≤−16 K (15 min)−1], turbulence is more common, with the conditional probability of turbulence exceeding the conditional probability of no turbulence. Aircraft with flight levels that are less than 8000 ft (~2440 m) above the cloud height also have a higher conditional probability of turbulence than no turbulence. Overall, the Bayesian scheme is found to be more skillful when compared with use of climatological information alone.

Full access
Justin M. Sieglaff, Lee M. Cronce, and Wayne F. Feltz

Abstract

The use of geostationary satellites for monitoring the development of deep convective clouds has been recently well documented. One such approach, the University of Wisconsin Cloud-Top Cooling Rate (CTC) algorithm, utilizes frequent Geostationary Operational Environmental Satellite (GOES) observations to diagnose the vigor of developing convective clouds through monitoring cooling rates of infrared window brightness temperature imagery. The CTC algorithm was modified to include GOES visible optical depth retrievals for the purpose of identifying growing convective clouds in regions of thin cirrus clouds. An automated objective skill analysis of the two CTC versions (with and without the GOES visible optical depth) versus a variety of Next Generation Weather Radar (NEXRAD) fields was performed using a cloud-object tracking system developed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies. The skill analysis was performed in a manner consistent with a recent study employing the same cloud-object tracking system. The analysis indicates that the inclusion of GOES visible optical depth retrievals in the CTC algorithm increases probability of detection and critical success index scores for all NEXRAD fields studied and slightly decreases false alarm ratios for most NEXRAD thresholds. In addition to better identifying vertically growing storms in regions of thin cirrus clouds, the analysis further demonstrates that the strongest cooling rates associated with developing convection are more reliably detected with the inclusion of visible optical depth and that storms that achieve intense reflectivity and large radar-estimated hail exhibit strong cloud-top cooling rates in much higher proportions than they do without the inclusion of visible optical depth.

Full access
Justin M. Sieglaff, Daniel C. Hartung, Wayne F. Feltz, Lee M. Cronce, and Valliappa Lakshmanan

Abstract

Studying deep convective clouds requires the use of available observation platforms with high temporal and spatial resolution, as well as other non–remote sensing meteorological data (i.e., numerical weather prediction model output, conventional observations, etc.). Such data are often at different temporal and spatial resolutions, and consequently, there exists the need to fuse these different meteorological datasets into a single framework. This paper introduces a methodology to identify and track convective cloud objects from convective cloud infancy [as few as three Geostationary Operational Environmental Satellite (GOES) infrared (IR) pixels] into the mature phase (hundreds of GOES IR pixels) using only geostationary imager IR window observations for the purpose of monitoring the initial growth of convective clouds.

The object tracking system described within builds upon the Warning Decision Support System-Integrated Information (WDSS-II) object tracking capabilities. The system uses an IR-window-based field as input to WDSS-II for cloud object identification and tracking and a Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin (UW-CIMSS)-developed postprocessing algorithm to combine WDSS-II cloud object output. The final output of the system is used to fuse multiple meteorological datasets into a single cloud object framework. The object tracking system performance analysis shows improved object tracking performance with both increased temporal resolution of the geostationary data and increased cloud object size. The system output is demonstrated as an effective means for fusing a variety of meteorological data including raw satellite observations, satellite algorithm output, radar observations, and derived output, numerical weather prediction model output, and lightning detection data for studying the initial growth of deep convective clouds and temporal trends of such data.

Full access
Daniel C. Hartung, Justin M. Sieglaff, Lee M. Cronce, and Wayne F. Feltz

Abstract

The University of Wisconsin Convective Initiation (UWCI) algorithm utilizes geostationary IR satellite data to compute cloud-top cooling (UW-CTC) rates and assign CI nowcasts to vertically growing clouds. This study is motivated by National Weather Service (NWS) forecaster reviews of the algorithm output, which hypothesized that more intense cloud-top cooling corresponds to more vigorous short-term (0–60 min) convective development. An objective validation of UW-CTC rates using a satellite-based object-tracking methodology is presented, along with a prognostic evaluation of such cloud-top cooling rates for use in forecasting the growth and development of deep convection. In general, both a cloud object’s instantaneous and maximum cooling rate(s) are shown to be useful prognostic tools in predicting future radar intensification. UW-CTC rates are shown to be most skillful in detecting convective clouds that achieved intense radar signatures. The UW-CTC rate lead time ahead of the various radar fields is also shown, along with an illustration of the benefit of UW-CTC rates in operational forecasting. The results of this study suggest that convective clouds with the strongest UW-CTC rates are more likely to achieve significant near-term (0–60 min) radar signatures in such fields as composite reflectivity, vertically integrated liquid (VIL), and maximum estimated size of hail (MESH) compared to clouds that exhibit only weak UW-CTC rates.

Full access
John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, and Andrew K. Heidinger

Abstract

Geostationary satellites [e.g., the Geostationary Operational Environmental Satellite (GOES)] provide high temporal resolution of cloud development and motion, which is essential to the study of many mesoscale phenomena, including thunderstorms. Initial research on thunderstorm growth with geostationary imagery focused on the mature stages of storm evolution, whereas more recent research on satellite-observed storm growth has concentrated on convective initiation, often defined arbitrarily as the presence of a given radar echo threshold. This paper seeks to link the temporal trends in robust GOES-derived cloud properties with the future occurrence of severe-weather radar signatures during the development phase of thunderstorm evolution, which includes convective initiation. Two classes of storms (severe and nonsevere) are identified and tracked over time in satellite imagery, providing distributions of satellite growth rates for each class. The relationship between the temporal trends in satellite-derived cloud properties and Next Generation Weather Radar (NEXRAD)-derived storm attributes is used to show that this satellite-based approach can potentially be used to extend severe-weather-warning lead times (with respect to radar-derived signatures), without a substantial increase in false alarms. In addition, the effect of varying temporal sampling is investigated on several storms during a period of GOES super-rapid-scan operations (SRSOR). It is found that, from a satellite perspective, storms evolve significantly on time scales shorter than the current GOES operational scan strategies.

Full access
Justin M. Sieglaff, Timothy J. Schmit, W. Paul Menzel, and Steven A. Ackerman

Abstract

A high spectral resolution geostationary sounder can make spectrally detailed measurements of the infrared spectrum at high temporal resolution, which provides unique information about the lower-tropospheric temperature and moisture structure. Within the infrared window region, many spectrally narrow, relatively weak water vapor absorption lines and one carbon dioxide absorption line exist. Frequent measurement of these absorption lines can provide critical information for monitoring the evolution of the lower-tropospheric thermodynamic state. This can improve short-term convective forecasts by monitoring regions of changing atmospheric stability. While providing valuable observations, the current geostationary sounders are spectrally broad and do not resolve the important spectrally narrow absorption lines needed to observe the planetary boundary layer. The usefulness of high spectral resolution measurements from polar-orbiting instruments has been shown in the literature, as has the usefulness of high temporal resolution measurements from geostationary instruments. Little attention has been given to the combination of high temporal along with high spectral resolution measurements. This paper demonstrates the potential utility of high temporal and high spectral resolution infrared radiances.

Full access
John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Anthony Wimmers, Jason Brunner, and Willard Bellon

Abstract

Intense thunderstorms threaten life and property, impact aviation, and are a challenging forecast problem, particularly without precipitation-sensing radar data. Trained forecasters often look for features in geostationary satellite images such as rapid cloud growth, strong and persistent overshooting tops, U- or V-shaped patterns in storm-top temperature (and associated above-anvil cirrus plumes), thermal couplets, intricate texturing in cloud albedo (e.g., “bubbling” cloud tops), cloud-top divergence, spatial and temporal trends in lightning, and other nuances to identify intense thunderstorms. In this paper, a machine-learning algorithm was employed to automatically learn and extract salient features and patterns in geostationary satellite data for the prediction of intense convection. Namely, a convolutional neural network (CNN) was trained on 0.64-μm reflectance and 10.35-μm brightness temperature from the Advanced Baseline Imager (ABI) and flash-extent density (FED) from the Geostationary Lightning Mapper (GLM) on board GOES-16. Using a training dataset consisting of over 220 000 human-labeled satellite images, the CNN learned pertinent features that are known to be associated with intense convection and skillfully discriminated between intense and ordinary convection. The CNN also learned a more nuanced feature associated with intense convection—strong infrared brightness temperature gradients near cloud edges in the vicinity of the main updraft. A successive-permutation test ranked the most important predictors as follows: 1) ABI 10.35-μm brightness temperature, 2) ABI GLM flash-extent density, and 3) ABI 0.64-μm reflectance. The CNN model can provide forecasters with quantitative information that often foreshadows the occurrence of severe weather, day or night, over the full range of instrument-scan modes.

Restricted access
John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Lee Cronce, and Jason Brunner

ABSTRACT

Severe convective storms are hazardous to both life and property and thus their accurate and timely prediction is imperative. In response to this critical need to help fulfill the mission of the National Oceanic and Atmospheric Administration (NOAA), NOAA and the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin (UW) have developed NOAA ProbSevere—an operational short-term forecasting subsystem within the Multi-Radar Multi-Sensor (MRMS) system, providing storm-based probabilistic guidance to severe convective hazards. ProbSevere extracts and integrates pertinent data from a variety of meteorological sources via multiplatform multiscale storm identification and tracking in order to compute severe hazard probabilities in a statistical framework, using naïve Bayesian classifiers. Version 1 of ProbSevere (PSv1) employed one model—the “probability of any severe hazard” trained on the U.S. National Weather Service (NWS) criteria. Version 2 of ProbSevere (PSv2) implements four models, three naïve Bayesian classifiers trained to specific hazards: 1) severe hail, 2) severe straight-line wind gusts, 3) tornadoes; and a combined model for any of the aforementioned hazards, which takes the maximum probability of the three classifiers. This paper overviews the ProbSevere system and details the construction and selection of predictors for the models. An evaluation of the four models demonstrated that v2 is more skillful than v1 for each severe hazard with higher critical success index scores and that the optimal probability threshold varies by region of the United States. The discussion highlights PSv2 in NOAA’s Hazardous Weather Testbed (HWT) and current and future research for convective nowcasting.

Free access
John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, and Daniel T. Lindsey

Abstract

The formation and maintenance of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to observe. Given inherent uncertainties in storm development, it is intuitive to predict severe storms in a probabilistic manner. This paper presents such an approach to forecasting severe thunderstorms and their associated hazards, fusing together data from several sources as input into a statistical model. Mesoscale numerical weather prediction (NWP) models have been developed in part to forecast environments favorable to severe storm development. Geostationary satellites, such as the Geostationary Operational Environmental Satellite (GOES) series, maintain a frequently updating view of growing cumulus clouds over the contiguous United States to provide temporal trends in developing convection to forecasters. The Next Generation Weather Radar (NEXRAD) network delivers repeated scans of hydrometeors inside storms, monitoring the intensification of hydrometeor size and extent, as well as hydrometeor motion. Forecasters utilize NWP models, and GOES and NEXRAD data, at different stages of the forecast of severe storms, and the model described in this paper exploits data from each in an attempt to predict severe hazards in a more accurate and timely manner while providing uncertainty information to the forecaster. A preliminary evaluation of the model demonstrates good skill in the forecast of storms, and also displays the potential to increase lead time on severe hazards, as measured relative to the issuance times of National Weather Service (NWS) severe thunderstorm and tornado warnings and occurrence times of severe events in local storm reports.

Full access
Sarah M. Griffin, Jason A. Otkin, Christopher M. Rozoff, Justin M. Sieglaff, Lee M. Cronce, and Curtis R. Alexander

Abstract

In this study, the utility of dimensioned, neighborhood-based, and object-based forecast verification metrics for cloud verification is assessed using output from the experimental High Resolution Rapid Refresh (HRRRx) model over a 1-day period containing different modes of convection. This is accomplished by comparing observed and simulated Geostationary Operational Environmental Satellite (GOES) 10.7-μm brightness temperatures (BTs). Traditional dimensioned metrics such as mean absolute error (MAE) and mean bias error (MBE) were used to assess the overall model accuracy. The MBE showed that the HRRRx BTs for forecast hours 0 and 1 are too warm compared with the observations, indicating a lack of cloud cover, but rapidly become too cold in subsequent hours because of the generation of excessive upper-level cloudiness. Neighborhood and object-based statistics were used to investigate the source of the HRRRx cloud cover errors. The neighborhood statistic fractions skill score (FSS) showed that displacement errors between cloud objects identified in the HRRRx and GOES BTs increased with time. Combined with the MBE, the FSS distinguished when changes in MAE were due to differences in the HRRRx BT bias or displacement in cloud features. The Method for Object-Based Diagnostic Evaluation (MODE) analyzed the similarity between HRRRx and GOES cloud features in shape and location. The similarity was summarized using the newly defined MODE composite score (MCS), an area-weighted calculation using the cloud feature match value from MODE. Combined with the FSS, the MCS indicated if HRRRx forecast error is the result of cloud shape, since the MCS is moderately large when forecast and observation objects are similar in size.

Full access