2015 STAR Seminars

Super-ensemble statistical precipitation forecasting is evaluated for the contiguous US. Ensemble statistical forecasting combines a number of individual forecasts of some property such as precipitation. A super-ensemble forecast considers the errors of individual forecasts in weighting them to form the ensemble. Here short-term precipitation forecast are considered to test the methods. Cross-validation is used to evaluate forecast skill. Some tests use SST predictors and some evaluate the impact of predictions including ocean-area precipitation predictors. Although predictions are heavily influenced by ENSO variations, other regions contribute greatly to forecast skill. The super- ensemble method improves skill because it combines good forecasts for different regions from different predictors. The super ensemble is also used to optimally combine forecasts from several statistical models with different qualities. Since multiple regions and predictors contribute to skill, the ENSO spring barrier is reduced in the ensemble. Using satellite-based ocean-area precipitation predictors further increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecasts are different. That suggests that the statistical and dynamic forecasts may be combined in a larger super ensemble to yield further improvements.

Biosketch of the Speaker:

Thomas Smith is a physical scientist with NOAA, NESDIS, STAR and CICS. His undergraduate degree is in mathematics from Rutgers University. In postgraduate studies he received a MS in meteorology, again from Rutgers, and a PhD in oceanography from the University of Delaware. His association with NOAA began with a UCAR postdoctoral fellowship at NOAA\'s Climate Prediction Center in 1990. That led to a NOAA job at CPC where he performed ocean and climate analyses for the center. In late 2000 he followed Dick Reynolds to NOAA/NCDC to continue with their collaborations. In 2007 he moved to NOAA/STAR where he works on various climate and satellite analyses.

The undeniable success of altimetry over the open ocean - where it has allowed a quantum leap in our knowledge of ocean dynamics and the first truly global quantification of the rate of sea level changes - has stimulated in recent years a lively community of researchers to develop techniques that improve estimates of altimetric quantities in the coastal strip, trying to remove some of the obstacles that traditionally caused the data to be flagged as bad and discarded.

This Coastal Altimetry Community ( http://www.coastalt.eu/community ) has worked (and is working) hard on both technical and application-related issues such as:

• Retracking of radar echoes affected by contamination by land and/or bright returns in the coastal zone;
• Improvement of the corrections for atmospheric, surface and other geophysical effects;
• Generation and validation of improved datasets;
• Design, implementation and testing of new applications.

The availability of SAR altimetry (from CryoSat, and soon from Sentinel-3) and Ka-band altimetry (from SARAL/AltiKa), both particularly well-suited to coastal zone observations, has provided further impetus to this field. Since 2008 progress in coastal altimetry has been showcased at annual Coastal Altimetry Workshops (CAWs), which constitute a lively forum for a community- led review of the science and applications of coastal altimetry, from data processing through emerging applications to new technologies. Results and recommendations from CAWs are then reported to the Ocean Surface Topography Science Team (OSTST). On 18-19 October 2015 the community will hold its 9th CAW, in Reston, VA, USA.

In this contribution I will summarize the main advances in the field, in terms of new processing techniques, new corrections, new datasets, new applications. I will highlight the synergies with other components of coastal observing and modeling systems with the aim of stimulating a discussion on how further exploit the combination of modeling and observational tools. The intrinsically higher resolution afforded by SAR and Ka-band altimetry, combined with the knowledge we are gaining on how to exploit this resolution in the coastal zone, pave the way also to a more complete characterization of the oceanic sub-mesoscales over the open ocean, which in turn may help with the interpretation of biophysical interactions of relevance to climate research.

The International Hydrographic Office C-55 publication addresses the need to improve the collection, quality and availability of hydrographic data world-wide, while also monitoring and rectifying possible deficiencies and shortcomings that are presented on the chart. This task of evaluating the adequacy of nautical chart products poses a challenge to many national hydrographic offices. This stems from the dearth of readily available spatial information: namely, the lack of reliable and accessible vessel traffic data, and little means to assess the changing nature of both near-shore bathymetry and shoreline in a simple and reliable manner.

A workshop was designed and developed internally at NOAA headquarters by Dr. Shachak Pe'eri and LTJG Anthony Klemm for International Hydrographic offices to use publically-available information. The goals of this workshop are: 1. Develop a chart adequacy assessment procedure using automatic-identification system (AIS) data and satellite-derived bathymetry (SDB) that can be applied in OCS. The procedure will be low cost and could be readily applied by HO's worldwide. 2. Train an international group of hydrographers through a two day workshop in Silver Spring, MD in summer 2015. 3. Replicate the methodology internationally 4. Develop a globally recognized, documented procedure for assessing chart adequacy based on the depth, main traffic routes and the last available survey in the area (without ranking based on regional/local geo-political prioritization, e.g., tourism or military).

Also in attendance will be GEBCO Scholar students and workshop participants who may give short summaries of their work

The Cross-track Infrared Sounder (CrIS) on the Suomi National Polar-orbiting Partnership (SNPP) and future Joint Polar Satellite System (JPSS) is a Fourier transform spectrometer that provides sounding information of the atmosphere over 3 wavelength ranges: LWIR (9.14 - 15.38 µm); MWIR (5.71 - 8.26 µm); and SWIR (3.92 - 4.64 µm). Since it was launched on 29 October 2011, extensive post-launch calibration and validation activities have been carried out by CrIS sensor data record team (SDR), leading to the validated level maturity level of CrIS SDR product in February 2014. In the presentation, the speaker will review how the inter-calibration method, which compares CrIS measurements with those from other satellite sensors, provides critical support to the CrIS radiometric, spectral, and geometric calibration as well as instrument characterization and algorithms improvements.

In the first part, the speaker will brief CrIS instrument and measurement characteristics, sensor Data Record (SDR) processing algorithm, postlaunch calibration activities and performance, and CrIS SDR data quality. Since the beginning of the mission, calibration and validation activities performed by the CrIS SDR team have led to the achievements of the validated level of the CrIS SDR product. This part of the presentation summarizes the results from the SDR quality assessment work for achieving validated product status. The purpose is to provide a comprehensive overview of CrIS instrument and how the CrIS instrument is calibrated on-orbit.

In the second part, the speaker will illustrate examples how inter-calibration supports for the CrIS post-launch calibration. The basic principle of inter-calibration is to compare two instruments when they view the same target at the same time, with the same spatial and spectral responses and the same viewing geometry. In order to achieve this goal, a series of thresholds is applied to collocate the data and transform it to a comparable scale. The purpose of inter-calibration is to quantify the instrument bias relative to the references instrument and thus to find the causes of biases and eliminate them from the instrument directly. During the CrIS postlaunch calibration and validation, CrIS hyperspectral radiance measurements are compared with the Atmospheric Infrared Sounder (AIRS) on NASA Aqua and Infrared Atmospheric Sounding Interferometer (IASI) on Metop-A and -Bto examine spectral and radiometric consistence and differences among three hyperspectral IR sounders. For geolocation validation, the geolocation fields of the Visible Infrared Imager Radiometer Suite (VIIRS) image bands are chosen a reference to access CrIS geometric calibration accuracy. In addition, inter-calibration also plays an important role for future JPSS CrIS algorithm improvements.

Speaker biography:
Likun Wang received the B.S. degree in atmospheric sciences and the M.S. degree in meteorology from Peking University, Beijing, China, in 1996 and 1999, respectively, and the Ph.D. degree in atmospheric sciences from University of Alaska Fairbanks, in 2004. He currently is an assistant research scientist with ESSIC/CICS in support of satellite sensor calibration and validation program for NOAA/NESDIS/STAR. Before that, he worked on lidar/radar remote sensing of clouds as a postdoctoral research associate with University of Maryland from 2004 to 2005. His current principal areas of interest include: 1) improving accuracy and preciseness of satellite measurements and products through calibration and validation efforts; and 2) recalibrating NOAA's historic satellite data records to create consistent, homogeneous long-term satellite measurements for climate studies.

STAR bio

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles, and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are the Advance Technology Microwave Sounder (ATMS) aboard S-NPP and Advanced Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS) aboard POES and Metop satellites. ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm was developed in a project supported by the JPSS PGRR program. Much improvement has been made since the original ATMS SFR algorithm was developed. A major advancement is the addition of a cold temperature component for snowfall detection. It extends the 2-meter temperature low limit for SFR from about 22°F to about 7°F, and drastically increases the probability of detection of snowfall in colder weather. Other algorithm development includes increasing accuracy of snowfall rate retrieval by taking into account snow microphysics and performing histogram matching with radar and gauge snowfall data.

The ATMS SFR, along with AMSU/MHS SFR, is being evaluated at several NWS Weather Forecast Offices (WFOs) in operational environment. This is a collaborative effort by NOAA, NASA SPoRT, and Cooperative Institute of Climate and Satellites (CICS) at University of Maryland. Feedback from a previous assessment of AMSU/MHS SFR indicated that latency was a major factor limiting the application of SFR in operations. Consequently, the project team developed the capability to utilize Direct Broadcast (DB) data for the SFR assessment this year. A processing system was built to acquire DB ATMS Level-1B data from University of Wisconsin and University of Alaska, retrieve SFR product, post product image on the project webpage, and send the SFR data to SPoRT within 30 minutes of satellite observation. SPoRT reformats the data to AWIPS/ AWIPS II and disseminates the product to WFOs via LDM. The project team collaborated with SPoRT to develop SFR training materials and conduct teletraining sessions prior to the product evaluation period. The presentation will include some case studies with forecaster feedback on the usefulness of the product and on issues that require future development.

Volcanic and severe weather applications illustrate the importance of developing sophisticated, scientific, computer algorithms to convert extremely large volumes of environmental data into actionable information needed to help mitigate hazards and increase environmental intelligence. The need for science based computer algorithms has never been greater as data volumes and information content will increase significantly with NOAA's next generation of operational satellites, GOES-R and JPSS.

Recent volcanic eruptions and the subsequent disruption of global air traffic have garnered considerable public attention. NOAA, in collaboration with the University of Wisconsin, have developed innovative methods of detecting and characterizing volcanic ash clouds from space, and new satellite products are now used by Volcanic Ash Advisory Centers (VAAC) in the U.S., Australia, and elsewhere. The products are used to increase the timeliness and accuracy of volcanic ash advisories, which are used by air traffic controllers to divert aircraft around hazards.

In addition, thunderstorms that produce large hail, damaging winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to directly observe. To address this challenge NOAA and the University of Wisconsin developed a statistical, data driven, severe weather prediction model. The model, know as ProbSevere, utilizes satellite, radar, and numerical weather prediction data to determine the probability that a developing thunderstorm will produce severe weather. At NOAA's Hazardous Weather Testbed, ProbSevere was shown to increase the timeliness and accuracy of severe weather warnings. Beginning in the spring of 2015 several National Weather Service forecast offices will utilize ProbSevere in operations.

The presentation linked above includes summaries of presentations given at AMS by the following STAR scientists: