Search Results

You are looking at 1 - 10 of 11 items for

  • Author or Editor: Jennifer S. Haase x
  • Refine by Access: All Content x
Clear All Modify Search
Shu-Hua Chen, Zhan Zhao, Jennifer S. Haase, Aidong Chen, and Francois Vandenberghe

Abstract

This study determined the accuracy and biases associated with retrieved Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water (TPW) data, and it investigated the impact of these data on severe weather simulations using the Weather Research and Forecast (WRF) model. Comparisons of MODIS TPW with the global positioning system (GPS) TPW and radiosonde-derived TPW were carried out. The comparison with GPS TPW over the United States showed that the root-mean-square (RMS) differences between these two datasets were about 5.2 and 3.3 mm for infrared (IR) and near-infrared (nIR) TPW, respectively. MODIS IR TPW data were overestimated in a dry atmosphere but underestimated in a moist atmosphere, whereas the nIR values were slightly underestimated in a dry atmosphere but overestimated in a moist atmosphere.

Two cases, a severe thunderstorm system (2004) over land and Hurricane Isidore (2002) over ocean, as well as conventional observations and Special Sensor Microwave Imager (SSM/I) retrievals were used to assess the impact of MODIS nIR TPW data on severe weather simulations. The assimilation of MODIS data has a slightly positive impact on the simulated rainfall over Oklahoma for the thunderstorm case, and it was able to enhance Isidore’s intensity when the storm track was reasonably simulated. The use of original and bias-corrected MODIS nIR TPW did not show significant differences from both case studies. In addition, SSM/I data were found to have a positive impact on both severe weather simulations, and the impact was comparable to or slightly better than that of MODIS data.

Full access
Gian Villamil-Otero, Ryan Meiszberg, Jennifer S. Haase, Ki-Hong Min, Mark R. Jury, and John J. Braun

Abstract

To investigate topographic–thermal circulations and the associated moisture variability over western Puerto Rico, field data were collected from 15 to 31 March 2011. Surface meteorological instruments and ground-based GPS receivers measured the circulation and precipitable water with high spatial and temporal resolution, and the Weather Research and Forecasting (WRF) Model was used to simulate the mesoscale flow at 1-km resolution. A westerly onshore flow of ~4 m s−1 over Mayaguez Bay was observed on many days, due to an interaction between thermally driven [3°C (10 km)−1] sea-breeze circulation and an island wake comprised of twin gyres. The thermally driven sea breeze occurred only when easterly synoptic winds favorably oriented the gyres with respect to the coast. Moisture associated with onshore flow was characterized by GPS measured precipitable water (PW). There is diurnal cycling of PW > 3 cm over the west coast during periods of onshore flow. The WRF Model tends to overestimate PW on the west side of the island, suggesting evapotranspiration as a process needing further attention. Fluctuations of PW affect local rainfall in times of convective instability.

Full access
Weixing Zhang, Yidong Lou, Jennifer S. Haase, Rui Zhang, Gang Zheng, Jinfang Huang, Chuang Shi, and Jingnan Liu

Abstract

Global positioning system (GPS) data from over 260 ground-based permanent stations in China covering the period from 1 March 1999 to 30 April 2015 were used to estimate precipitable water (PW) above each site with an accuracy of about 0.75 mm. Four types of radiosondes (referred to as GZZ2, GTS1, GTS1-1, and GTS1-2) were used in China during this period. Instrumentation type changes in radiosonde records were identified by comparing PW calculated from GPS and radiosonde data. Systematic errors in different radiosonde types introduced significant biases to the estimated PW trends at stations where more than one radiosonde type was used. Estimating PW trends from reanalysis products (ERA-Interim), which assimilate the unadjusted radiosonde humidity data, resulted in an artificial downward PW trend at almost all stations in China. The statistically significant GPS PW trends are predominantly positive, consistent in sign with the increase in moisture expected from the Clausius–Clapeyron relation due to a global temperature increase. The standard deviations of the differences between ERA-Interim and GPS PW in the summer were 3 times larger than the observational error of GPS PW, suggesting that potentially significant improvements to the reanalysis could be achieved by assimilating denser GPS PW observations over China. This work, based on an entirely independent GPS PW dataset, confirms previously reported significant differences in radiosonde PW trends when using corrected data. Furthermore, the dense geographical coverage of the all-weather GPS PW observations, especially in remote areas in western China, provides a valuable resource for calibrating regional trends in reanalysis products.

Full access
Minghua Zheng, Luca Delle Monache, Xingren Wu, F. Martin Ralph, Bruce Cornuelle, Vijay Tallapragada, Jennifer S. Haase, Anna M. Wilson, Matthew Mazloff, Aneesh Subramanian, and Forest Cannon

Capsule summary

A significant data void exists in the lower atmosphere during Atmospheric River (AR) events over the northeastern Pacific. When available, AR Reconnaissance data provide the majority of direct observations within the critical layer of an oceanic AR.

Full access
Angelyn W. Moore, Ivory J. Small, Seth I. Gutman, Yehuda Bock, John L. Dumas, Peng Fang, Jennifer S. Haase, Mark E. Jackson, and Jayme L. Laber

Abstract

During the North American Monsoon, low-to-midlevel moisture is transported in surges from the Gulf of California and Eastern Pacific Ocean into Mexico and the American Southwest. As rising levels of precipitable water interact with the mountainous terrain, severe thunderstorms can develop, resulting in flash floods that threaten life and property. The rapid evolution of these storms, coupled with the relative lack of upper-air and surface weather observations in the region, make them difficult to predict and monitor, and guidance from numerical weather prediction models can vary greatly under these conditions. Precipitable water vapor (PW) estimates derived from continuously operating ground-based GPS receivers have been available for some time from NOAA’s GPS-Met program, but these observations have been of limited utility to operational forecasters in part due to poor spatial resolution. Under a NASA Advanced Information Systems Technology project, 37 real-time stations were added to NOAA’s GPS-Met analysis providing 30-min PW estimates, reducing station spacing from approximately 150 km to 30 km in Southern California. An 18–22 July 2013 North American Monsoon event provided an opportunity to evaluate the utility of the additional upper-air moisture observations to enhance National Weather Service (NWS) forecaster situational awareness during the rapidly developing event. NWS forecasters used these additional data to detect rapid moisture increases at intervals between the available 1–6-h model updates and approximately twice-daily radiosonde observations, and these contributed tangibly to the issuance of timely flood watches and warnings in advance of flash floods, debris flows, and related road closures.

Full access
Minghua Zheng, Luca Delle Monache, Xingren Wu, F. Martin Ralph, Bruce Cornuelle, Vijay Tallapragada, Jennifer S. Haase, Anna M. Wilson, Matthew Mazloff, Aneesh Subramanian, and Forest Cannon

Abstract

Conventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean are sparse. Satellite radiances are affected by the presence of clouds and heavy precipitation, which impact their distribution in the lower atmosphere and in precipitating areas. The goal of this study is to document a data gap in existing observations of ARs in the northeastern Pacific, and to investigate how a targeted field campaign called AR Reconnaissance (AR Recon) can effectively fill this gap. When reconnaissance data are excluded, there is a gap in AR regions from near the surface to the middle troposphere (below 450 hPa), where most water vapor and its transport are concentrated. All-sky microwave radiances provide data within the AR object, but their quality is degraded near the AR core and its leading edge, due to the existence of thick clouds and precipitation. AR Recon samples ARs and surrounding areas to improve downstream precipitation forecasts over the western United States. This study demonstrates that despite the apparently extensive swaths of modern satellite radiances, which are critical to estimate large-scale flow, the data collected during 15 AR Recon cases in 2016, 2018, and 2019 supply about 99% of humidity, 78% of temperature, and 45% of wind observations in the critical maximum water vapor transport layer from the ocean surface to 700 hPa in ARs. The high-vertical-resolution dropsonde observations in the lower atmosphere over the northeastern Pacific Ocean can significantly improve the sampling of low-level jets transporting water vapor to high-impact precipitation events in the western United States.

Full access
Michael T. Montgomery, Christopher Davis, Timothy Dunkerton, Zhuo Wang, Christopher Velden, Ryan Torn, Sharanya J. Majumdar, Fuqing Zhang, Roger K. Smith, Lance Bosart, Michael M. Bell, Jennifer S. Haase, Andrew Heymsfield, Jorgen Jensen, Teresa Campos, and Mark A. Boothe

The principal hypotheses of a new model of tropical cyclogenesis, known as the marsupial paradigm, were tested in the context of Atlantic tropical disturbances during the National Science Foundation (NSF)-sponsored Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) experiment in 2010. PREDICT was part of a tri-agency collaboration, along with the National Aeronautics and Space Administration's Genesis and Rapid Intensification Processes (NASA GRIP) experiment and the National Oceanic and Atmospheric Administration's Intensity Forecasting Experiment (NOAA IFEX), intended to examine both developing and nondeveloping tropical disturbances.

During PREDICT, a total of 26 missions were flown with the NSF/NCAR Gulfstream V (GV) aircraft sampling eight tropical disturbances. Among these were four cases (Fiona, ex-Gaston, Karl, and Matthew) for which three or more missions were conducted, many on consecutive days. Because of the scientific focus on the Lagrangian nature of the tropical cyclogenesis process, a wave-relative frame of reference was adopted throughout the experiment in which various model- and satellite-based products were examined to guide aircraft planning and real-time operations. Here, the scientific products and examples of data collected are highlighted for several of the disturbances. The suite of cases observed represents arguably the most comprehensive, self-consistent dataset ever collected on the environment and mesoscale structure of developing and nondeveloping predepression disturbances.

Full access
Yolande L. Serra, Jennifer S. Haase, David K. Adams, Qiang Fu, Thomas P. Ackerman, M. Joan Alexander, Avelino Arellano, Larissa Back, Shu-Hua Chen, Kerry Emanuel, Zeljka Fuchs, Zhiming Kuang, Benjamin R Lintner, Brian Mapes, David Neelin, David Raymond, Adam H. Sobel, Paul W. Staten, Aneesh Subramanian, David W. J. Thompson, Gabriel Vecchi, Robert Wood, and Paquita Zuidema

We are deeply concerned with the trend toward the purchase of commercial weather and climate data by the National Oceanic and Atmospheric Administration (NOAA) and other government agencies that may follow. One major concern is for the stability of both the raw and processed forms of these datasets as commercial enterprises come and go. Also of concern is the loss of free, publicly available data, which provide the backbone for much of the non-profit research at universities, government laboratories, and other nonprofit organizations. Related to this latter point is the questionable practice of private companies gaining the rights over data

Open access
F. Martin Ralph, Forest Cannon, Vijay Tallapragada, Christopher A. Davis, James D. Doyle, Florian Pappenberger, Aneesh Subramanian, Anna M. Wilson, David A. Lavers, Carolyn A. Reynolds, Jennifer S. Haase, Luca Centurioni, Bruce Ingleby, Jonathan J. Rutz, Jason M. Cordeira, Minghua Zheng, Chad Hecht, Brian Kawzenuk, and Luca Delle Monache

Abstract

Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort’s core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the “National Winter Season Operations Plan” to support improved outcomes for emergency preparedness and water management in the West.

Free access
Alison Cobb, F. Martin Ralph, Vijay Tallapragada, Anna M. Wilson, Christopher A. Davis, Luca Delle Monache, James D. Doyle, Florian Pappenberger, Carolyn A. Reynolds, Aneesh Subramanian, Peter G. Black, Forest Cannon, Chris Castellano, Jason M. Cordeira, Jennifer S. Haase, Chad Hecht, Brian Kawzenuk, David A. Lavers, Michael J. Murphy Jr., Jack Parrish, Ryan Rickert, Jonathan J. Rutz, Ryan Torn, Xingren Wu, and Minghua Zheng

Abstract

Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days.

The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership.

Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.

Restricted access