Publications

In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and several fields from the Rapid Refresh (RAP) 13 km numerical weather prediction model to create precipitation and echo top fields similar to those provided by existing Federal Aviation Administration (FAA) weather systems. Preprocessing and feature extraction methods are described to construct inputs for model training. A variety of machine learning algorithms are investigated to identify which provides the most accuracy. Output from the machine learning model is blended with existing radar mosaics to create weather radar-like analyses that extend into offshore regions. The resulting fields are validated using land radars and satellite precipitation measurements provided by the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) core observatory satellite. This capability is initially being developed for the Miami Oceanic airspace with the goal of providing improved situational awareness for offshore air traffic control.

Deterministic storm-scale weather forecasts, such as those generated from the FAA's 0-8 hour CoSPA system, are highly valuable to aviation traffic managers. They provide forecasted characteristics of storm structure, strength, orientation, and coverage that are very helpful for strategic planning purposes in the National Airspace System (NAS). However, these deterministic weather forecasts contain inherent uncertainty that varies with the general weather scenario at the forecast issue time, the predicted storm type, and the forecast time horizon. This uncertainty can cause large changes in the forecast from update to update, thereby eroding user confidence and ultimately reducing the forecast's effectiveness in the decision-making process. Deterministic forecasts generally lack objective measures of this uncertainty, making it very difficult for users of the forecast to know how much confidence to have in the forecast during their decision-making process. This presentation will describe a methodology to provide measures of confidence for deterministic storm-scale forecasts. The method inputs several characteristics of the current and historical weather forecasts, such as spatial scale, intensity, weather type, orientation, permeability, and run-to-run variability of the forecasts, into a statistical model to provide a measure of confidence in a forecasted quantity. In this work, the forecasted quantity is aircraft blockage associated with key high-impact Flow Constrained Areas (FCAs) in the NAS. The results from the method, which will also be presented, provide the user with a measure of forecast confidence in several blockage categories (none, low, medium, and high) associated with the FCAs. This measure of forecast confidence is geared toward helping en-route strategic planners in the NAS make better use of deterministic storm-scale weather forecasts for air traffic management.

Storm initiation is a very challenging aspect of nowcasting. Rapidly forming storms that appear in areas of little to no pre-existing convection can pose a danger to aircraft, and have the potential to cause unforeseen delays in the national airspace system (NAS). As such, detection and prediction of the initial development of convective storms is critical to NAS operations and planning. The Corridor Integrated Weather System (CIWS) currently provides deterministic 0-2 hour storm forecasts over the NAS, and represents the 0-2 hour portion of the 0-8 hour deterministic CoSPA storm forecasts. CIWS includes a convective initiation (CI) module, however this module has difficulty initiating convection in areas of little or no pre-existing convection. In this study, we seek to improve the capabilities of the CI module using machine learning methods to detect regions of imminent convection and enhance the storm initiation to the 0-2 hour forecast. Improvements to the current CI detection capabilities will prove to be a benefit in the short term, as well in the longer term plans of the Federal Aviation Administration's (FAA) Next Generation Air Transportation System (NextGen). In order to improve the capabilities of the CI module in CIWS, data from a variety of sources are fused together to produce a forecast of CI. Data incorporated into the CI algorithm include: Satellite fields from NASA's Satellite Convective Analysis and Tracking (SATCAST), convective instability fields, and a collection of numerical models which includes NOAA's North America Rapid Refresh Ensemble Time Lag System (NARRE-TL), the Localized Aviation MOS Program (LAMP), Short Range Ensemble Forecasts (SREF), and High Resolution Rapid Refresh (HRRR) model forecasts. These fields are brought together in a machine learning framework to create a probabilistic model which is used to initiate new growth in the deterministic CIWS 0-2 hour forecast. A variety of machine learning classifiers, including logistic regression, neural networks, support vector machines, and random forests, are used to investigate which technique works best with the data available. The skill of this updated CI capability is being assessed over the summer of 2012 using multiple skill metrics including CSI, bias and fraction skill score.

Air traffic congestion in the United States (US) is a serious national problem resulting in a critical need for timely, reliable and high quality forecasts of precipitation and echo tops with forecast time horizons of up to 8 hours. In order to address the short-term needs of the Federal Aviation Administration (FAA) as well as the long-term goals of the US's Next Generation Airspace System (NextGen), MIT Lincoln Laboratory, NCAR Research Applications Laboratory and NOAA Earth Systems Research Laboratory (ESRL) Global Systems Division (GSD) are collaborating on developing a forecast system under funding from the FAA's Aviation Weather Research Program (AWRP). The CoSPA system combines the latest technologies in heuristic nowcasting, extrapolation, statistical techniques and numerical weather prediction to produce rapidly updating (15 min) 0-8 hour forecasts of storm locations, echo tops and intensities. The system blends highly-skillful heuristic nowcasts with output from NOAA's High Resolution Rapid Refresh (HRRR) using phase correction and statistical weighting functions. The CoSPA 0-8 hour forecasts are accessible to the aviation community via an operational situation display and a website that builds upon the FAA's Corridor Integrated Weather System (CIWS) and shows current time situational awareness products including: VIL, echo tops, lightning, growth and decay, forecasts and verification contours, as well as an animation of the weather from 8 hours in the past to 8 hours into the future. This presentation will include a brief description of the CoSPA forecast system and display, examples of forecast performance, and provide an overview of recent enhancements to CoSPA as well as ongoing research.

Air traffic congestion caused by convective weather in the US has become a serious national problem. Several studies have shown that there is a critical need for timely, reliable and high quality forecasts of precipitation and echo tops with forecast time horizons of up to 12 hours in order to predict airspace capacity (Robinson et al. 2008, Evans et al. 2006 and FAA REDAC Report 2007). Yet, there are currently several forecast systems available to strategic planners across the National Airspace System (NAS) that are not fully meeting Air Traffic Management (ATM) needs. Furthermore, the use of many forecasting systems increases the potential for conflicting information in the planning process, which can cause situational awareness problems between operational facilities. One of the goals of the Next Generation Air Transportation System (NextGen) is to consolidate these redundant and sometimes conflicting forecast systems into a Single Authoritative Source (SAS) for aviation uses. The FAA initiated an effort to begin consolidating these systems in 2006, which led to the establishment of a collaboration between MIT Lincoln Laboratory (MIT LL), the National Center for Atmospheric Research (NCAR) Research Applications Laboratory (RAL), the NOAA Earth Systems Research Laboratory (ESRL) Global Systems Division (GSD) and NASA, called the Consolidated Storm Prediction for Aviation (CoSPA; Wolfson et al. 2008). The on-going collaboration is structured to leverage the expertise and technologies of each laboratory to build a CoSPA forecast capability that not only exceeds all current operational forecast capabilities and skill, but that provides enough resolution and skill to meet the demands of the envisioned NextGen decision support technology. The current CoSPA prototype for 0-6 hour forecasts is planned for operation as part of the NextGen Initial Operational Capability (IOC) in 2013. CoSPA is funded under the FAA's Aviation Weather Research Program (AWRP). The first CoSPA research prototype demonstration was conducted during the summer of 2008. Technologies from the Corridor Integrated Weather System (CIWS; Evans and Ducot 2006), National Convective Weather Forecast (NCWF; Megenhardt et al. 2004), and NOAA’s Rapid Update Cycle (RUC; Benjamin et al. 2004) and High Resolution Rapid Refresh (HRRR; Benjamin et al. 2009) models were consolidated along with new technologies into a single high-resolution forecast and display system. Historically, forecasts based on heuristics and extrapolation have performed well in the 0-2 hour window, whereas forecasts based on Numerical Weather Prediction (NWP) models have shown better performance than heuristics past 3-4 hours (Figure 1). One of the goals of CoSPA is to optimally blend heuristics and NWP models into a unified set of aviation-specific storm forecast products with the best overall performance possible. The CoSPA prototype demonstration began in July 2008 with 2-6 hr forecasts of Vertically-Integrated Liquid water (VIL) that seamlessly matched with the 0-2 hr VIL forecasts available in CIWS. These real-time forecasts have been made available to the research team and FAA management only through a web-based interface. This paper discusses the system infrastructure, the forecast display, the forecast technology and performance of the 2-6 hr VIL forecast. Our early assessment based on the 2008 demonstration is that CoSPA is showing tremendous promise for greatly improving strategic storm forecasts for the NAS. Early user feedback during CoSPA briefings suggested that the 6 hr forecast time horizon be extended to 8 hours to better meet their planning functions, and that forecasts of Echo Tops must also be included.

The primary fields that provide weather situational awareness in the Corridor Integrated Weather System (CIWS) are radar-derived vertically-integrated liquid (VIL) and echo top height (ET). In situations of reduced or non-existent radar coverage, such as over the oceans, in mountainous terrain or during periods of radar outages, the radar VIL and ET fields are severely compromised or even absent. In these situations, the lightning data are often unaffected and fully available to use as a proxy for the radar fields in convective weather nowcasts. The purpose of this study is to develop the capability to utilize cloud-to-ground lightning strike data as a proxy for radar VIL and echo tops (ET) in the CIWS. The datasets used in this study are the National Lightning Detection Network (NLDN) and the 1 km/5min radar VIL and ET mosaics produced at MIT LL. To capture the synoptic variability of the lightning-VIL and lightning-ET relationships over the CIWS domain, atmospheric variables from the NOAA Rapid Update Cycle (RUC) model and the Space-time Mesoscale Analysis System (STMAS) are utilized with the lightning data in a statistical regression framework. Once spatially and temporally coherent regions of VIL and ET derived from the lightning are produced, the potential exists for tracking these regions and providing accurate short-term forecasting of convective hazards.

This paper describes the Tactical 0-2 hour Convective Weather Forecast (CWF) algorithm developed by the MIT LL for the FAA. We will address the algorithm and focus on the key scientific developments. Future directions will also be discussed.