Publications

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2017
Wang, KN, Garrison JL, Haase JS, Murphy BJ.  2017.  Improvements to GPS Airborne Radio Occultation in the Lower Troposphere Through Implementation of the Phase Matching Method. Journal of Geophysical Research-Atmospheres. 122:10215-10230.   10.1002/2017jd026568   AbstractWebsite

Airborne radio occultation (ARO) is a remote sensing technique for atmospheric sounding using Global Positioning System signals received by an airborne instrument. The atmospheric refractivity profile, which depends on pressure, temperature, and water vapor, can be retrieved by measuring the signal delay due to the refractive medium through which the signal traverses. The ARO system was developed to make repeated observations within an individual meteorological event such as a tropical storm, regardless of the presence of clouds and precipitation, and complements existing observation techniques such as dropsondes and satellite remote sensing. RO systems can suffer multipath ray propagation in the lower troposphere if there are strong refractivity gradients, for example, due to a highly variable moisture distribution or a sharp boundary layer, interfering with continuous carrier phase tracking as well as complicating retrievals. The phase matching method has now been adapted for ARO and is shown to reduce negative biases in the refractivity retrieval by providing robust retrievals of bending angle in the presence of multipath. The retrieval results are presented for a flight campaign in September 2010 for Hurricane Karl in the Caribbean Sea. The accuracy is assessed through comparison with the European Centre for Medium Range Weather Forecasts Interim Reanalysis. The fractional difference in refractivity can be maintained at a standard deviation of 2% from flight level down to a height of 2km. The phase matching method decreases the negative refractivity bias by as much as 4% over the classical geometrical optics retrieval method.

2016
Adhikari, L, Xie FQ, Haase JS.  2016.  Application of the full spectrum inversion algorithm to simulated airborne GPS radio occultation signals. Atmospheric Measurement Techniques. 9:5077-5087.   10.5194/amt-9-5077-2016   AbstractWebsite

With a GPS receiver on board an airplane, the airborne radio occultation (ARO) technique provides dense lower-tropospheric soundings over target regions. Large variations in water vapor in the troposphere cause strong signal multipath, which could lead to systematic errors in RO retrievals with the geometric optics (GO) method. The space-borne GPS RO community has successfully developed the full-spectrum inversion (FSI) technique to solve the multipath problem. This paper is the first to adapt the FSI technique to retrieve atmospheric properties (bending and refractivity) from ARO signals, where it is necessary to compensate for the receiver traveling on a non-circular trajectory inside the atmosphere, and its use is demonstrated using an end-to-end simulation system. The forward-simulated GPS L1 (1575.42 MHz) signal amplitude and phase are used to test the modified FSI algorithm. The ARO FSI method is capable of reconstructing the fine vertical structure of the moist lower troposphere in the presence of severe multipath, which otherwise leads to large retrieval errors in the GO retrieval. The sensitivity of the modified FSI-retrieved bending angle and refractivity to errors in signal amplitude and errors in the measured refractivity at the receiver is presented. Accurate bending angle retrievals can be obtained from the surface up to similar to 250m below the receiver at typical flight altitudes above the tropopause, above which the retrieved bending angle becomes highly sensitive to the phase measurement noise. Abrupt changes in the signal amplitude that are a challenge for receiver tracking and geometric optics bending angle retrieval techniques do not produce any systematic bias in the FSI retrievals when the SNR is high. For very low SNR, the FSI performs as expected from theoretical considerations. The 1% in situ refractivity measurement errors at the receiver height can introduce a maximum refractivity retrieval error of 0.5% (1 K) near the receiver, but the error decreases gradually to similar to 0.05% (0.1 K) near the surface. In summary, the ARO FSI successfully retrieves the fine vertical structure of the atmosphere in the presence of multipath in the lower troposphere.

2015
Moore, AW, Small IJ, Gutman SI, Bock Y, Dumas JL, Fang P, Haase JS, Jackson ME, Laber JL.  2015.  National Weather Service forecasters use GPS precipitable water vapor for enhanced situational awareness during the Southern California summer monsoon. Bulletin of the American Meteorological Society. 96:1867-1877.   10.1175/bams-d-14-00095.1   AbstractWebsite

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.

Melgar, D, Geng JH, Crowell BW, Haase JS, Bock Y, Hammond WC, Allen RM.  2015.  Seismogeodesy of the 2014 M(w)6.1 Napa earthquake, California: Rapid response and modeling of fast rupture on a dipping strike-slip fault. Journal of Geophysical Research-Solid Earth. 120:5013-5033.   10.1002/2015jb011921   AbstractWebsite

Real-time high-rate geodetic data have been shown to be useful for rapid earthquake response systems during medium to large events. The 2014 M(w)6.1 Napa, California earthquake is important because it provides an opportunity to study an event at the lower threshold of what can be detected with GPS. We show the results of GPS-only earthquake source products such as peak ground displacement magnitude scaling, centroid moment tensor (CMT) solution, and static slip inversion. We also highlight the retrospective real-time combination of GPS and strong motion data to produce seismogeodetic waveforms that have higher precision and longer period information than GPS-only or seismic-only measurements of ground motion. We show their utility for rapid kinematic slip inversion and conclude that it would have been possible, with current real-time infrastructure, to determine the basic features of the earthquake source. We supplement the analysis with strong motion data collected close to the source to obtain an improved postevent image of the source process. The model reveals unilateral fast propagation of slip to the north of the hypocenter with a delayed onset of shallow slip. The source model suggests that the multiple strands of observed surface rupture are controlled by the shallow soft sediments of Napa Valley and do not necessarily represent the intersection of the main faulting surface and the free surface. We conclude that the main dislocation plane is westward dipping and should intersect the surface to the east, either where the easternmost strand of surface rupture is observed or at the location where the West Napa fault has been mapped in the past.

2008
Chen, SH, Zhao Z, Haase JS, Chen AD, Vandenberghe F.  2008.  A study of the characteristics and assimilation of retrieved MODIS total precipitable water data in severe weather simulations. Monthly Weather Review. 136:3608-3628.   10.1175/2008mwr2384.1   AbstractWebsite

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.