Search Results

You are looking at 1 - 10 of 64 items for

  • Author or Editor: Christa Peters-Lidard x
  • Refine by Access: All Content x
Clear All Modify Search
Christa D. Peters-Lidard and Luke H. Davis

Abstract

During the Southern Great Plains 1997 Hydrology Experiment, a tethersonde system was deployed at the U.S. Department of Energy’s Atmospheric Radiation Measurement Cloud and Radiation Test Bed (ARM CART) central facility. Additional measurements included several surface flux stations at the central facility and radiosondes at the ARM CART central and boundary facilities. Combined, these data support an examination of regional flux estimates obtained via the atmospheric boundary layer conservation approach. Because the tethersonde was deployed successfully only under light to moderate wind conditions, the effects of advection on estimation of regional fluxes generally are found to be small. Consistent with previous studies, direct estimation of the sensible heat flux yields more accuracy than direct estimation of the latent heat flux. Use of available energy measured at surface flux stations along with the direct sensible heat flux estimates yields latent heat estimates of similar accuracy to those obtained for the sensible heat flux. It is observed that variability in the entrainment parameter exhibits a considerable diurnal cycle, presumably related to the interplay between buoyant and shear production of turbulent kinetic energy near the entrainment zone.

Full access
Andrew M. Badger, Christa Peters-Lidard, and Dalia B. Kirschbaum

Abstract

A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2 × 2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3 × 3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments.

Significance Statement

We wanted to see if there was a method in which remotely sensed precipitation observations could be validated at a near-global scale for land areas. Scientific literature is filled with studies that validate various precipitation datasets over local-to-regional scales, with very few extending beyond that domain. This study provides a robust first attempt at validating a global precipitation product at a global scale using changes in remotely sensed soil moisture as an independent proxy for precipitation presence/absence. While the method demonstrates that there is skill in using soil moisture as a tool to validate precipitation at the global scale, we find that there are still instances of a systemic bias for arid climate regimes. This method lays the groundwork for future studies to provide a comprehensive global validation in a globally consistent manner.

Restricted access
Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

Abstract

Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

Full access
Yudong Tian, Christa D. Peters-Lidard, and John B. Eylander

Abstract

A new approach to reduce biases in satellite-based estimates in real time is proposed and tested in this study. Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. Most of these efforts require timely availability of surface gauge measurements. The new proposed approach does not require gauge measurements in real time. Instead, the Bayesian logic is used to establish a statistical relationship between satellite estimates and gauge measurements from recent historical data. Then this relationship is applied to real-time satellite estimates when gauge data are not yet available. This new scheme is tested over the United States with six years of precipitation estimates from two real-time satellite products [i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product 3B42RT and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH)] and a gauge analysis dataset [i.e., the CPC unified analysis]. The first 4-yr period was used as the training period to establish a satellite–gauge relationship, which was then applied to the last 2 yr as the correction period, during which gauge data were withheld for training but only used for evaluation. This approach showed that satellite biases were reduced by 70%–100% for the summers in the correction period. In addition, even when sparse networks with only 600 or 300 gauges were used during the training period, the biases were still reduced by 60%–80% and 47%–63%, respectively. The results also show a limitation in this approach as it tends to overadjust both light and strong events toward more intermediate rain rates.

Full access
Sarah Ringerud, Christa Peters-Lidard, Joe Munchak, and Yalei You

Abstract

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

Full access
Di Wu, Christa Peters-Lidard, Wei-Kuo Tao, and Walter Petersen

Abstract

The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real-time forecasts were conducted utilizing the NASA-Unified Weather Research and Forecasting (NU-WRF) Model to support the daily weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are conducted with different soil initializations, one from the spatially interpolated North American Mesoscale Forecast System (NAM) and the other produced by the Land Information System (LIS) using daily analysis of bias-corrected stage IV data. Both forecasts are then compared with NAM, stage IV, and Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) to understand the impact of land surface initialization on the predicted precipitation. In general, both NU-WRF runs are able to reproduce individual peaks of precipitation at the right time. NU-WRF is also able to replicate a better rainfall spatial distribution compared with NAM. Further sensitivity tests show that the high-resolution runs (1 and 3 km) are able to better capture the precipitation event compared to its coarser-resolution counterpart (9 km). Finally, the two sets of NU-WRF simulations produce very close rainfall characteristics in bias, spatial and temporal correlation scores, and probability density function. The land surface initialization does not show a significant impact on short-term rainfall forecast, which is largely because of high soil moisture during the field campaign period.

Full access
Yalei You, Christa Peters-Lidard, Joseph Turk, Sarah Ringerud, and Song Yang

Abstract

Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) to estimate precipitation rate. This study presents a new idea that can be used to improve existing algorithms: using TB temporal variation from the microwave radiometer constellation. As a proof of concept, microwave observations from eight polar-orbiting satellites are utilized to derive . Results show that correlates more strongly with precipitation rate than the instantaneous TB. Particularly, the correlation with precipitation rate improved to −0.6 by using over the Rocky Mountains and north of 45°N, while the correlation is only −0.1 by using TB. The underlying reason is that largely eliminates the negative influence from snow-covered land, which frequently is misidentified as precipitation. Another reason is that is less affected by environmental variation (e.g., temperature, water vapor). Further analysis shows that the magnitude of the correlation between and precipitation rate is dependent on the satellite revisit frequency. Finally, it is shown that the retrieval results from are superior to that from TB, with the largest improvement in winter. Additionally, the retrieved precipitation rate over snow-covered regions by only using at 89 GHz agrees well with the ground radar observations, which opens new opportunities to retrieve precipitation in high latitudes for sensors with the highest frequency at ~89 GHz. This study implies that a geostationary microwave radiometer can significantly improve precipitation retrieval performance. It also highlights the importance of maintaining the current passive microwave satellite constellation.

Full access
Joseph A. Santanello Jr., Christa D. Peters-Lidard, Sujay V. Kumar, Charles Alonge, and Wei-Kuo Tao

Abstract

Land–atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed because of the complex interactions and feedbacks present across a range of scales. Furthermore, uncoupled systems or experiments [e.g., the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS)] may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land–atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting Model (WRF) has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The coevolution of these variables and the convective PBL are sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land–atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land–atmosphere coupling (LoCo) using the LIS–WRF system, which will serve as a test bed for future experiments to evaluate coupling diagnostics within the community.

Full access
Yudong Tian, Christa D. Peters-Lidard, Bhaskar J. Choudhury, and Matthew Garcia

Abstract

In this study, the recent work of Gottschalck et al. and Ebert et al. is extended by assessing the suitability of two Tropical Rainfall Measuring Mission (TRMM)-based precipitation products for hydrological land data assimilation applications. The two products are NASA’s gauge-corrected TRMM 3B42 Version 6 (3B42), and the satellite-only NOAA Climate Prediction Center (CPC) morphing technique (CMORPH). The two products were evaluated against ground-based rain gauge–only and gauge-corrected Doppler radar measurements. The analyses were performed at multiple time scales, ranging from annual to diurnal, for the period March 2003 through February 2006. The analyses show that at annual or seasonal time scales, TRMM 3B42 has much lower biases and RMS errors than CMORPH. CMORPH shows season-dependent biases, with overestimation in summer and underestimation in winter. This leads to 50% higher RMS errors in CMORPH’s area-averaged daily precipitation than TRMM 3B42. At shorter time scales (5 days or less), CMORPH has slightly less uncertainty, and about 10%–20% higher probability of detection of rain events than TRMM 3B42. In addition, the satellite estimates detect more high-intensity events, causing a remarkable shift in precipitation spectrum. Summertime diurnal cycles in the United States are well captured by both products, although the 8-km CMORPH seems to capture more diurnal features than the 0.25° CMORPH or 3B42 products. CMORPH tends to overestimate the amplitude of the diurnal cycles, particularly in the central United States. Possible causes for the discrepancies between these products are discussed.

Full access
Yudong Tian, Christa D. Peters-Lidard, Robert F. Adler, Takuji Kubota, and Tomoo Ushio

Abstract

Precipitation estimates from the Global Satellite Mapping of Precipitation (GSMaP) project are evaluated over the contiguous United States (CONUS) for the period of 2005–06. GSMaP combines precipitation retrievals from the Tropical Rainfall Measuring Mission satellite and other polar-orbiting satellites, and interpolates them with cloud motion vectors derived from infrared images from geostationary satellites, to produce a high-resolution dataset. Four other satellite-based datasets are also evaluated concurrently with GSMaP, to provide a better perspective. The new Climate Prediction Center (CPC) unified gauge analysis is used as the reference data. The evaluation shows that GSMaP does well in capturing the spatial patterns of precipitation, especially for summer, and that it has better estimation of precipitation amount over the eastern than over the western CONUS. Meanwhile, GSMaP shares many of the challenges common to other satellite-based products, including that it underestimates in winter and overestimates in summer. In winter, GSMaP has on average one-half less precipitation over the western region and one-third less over the eastern region, whereas in summer it has about three-quarters and one-quarter more estimated precipitation over the two respective regions, respectively. Most of the summer overestimates (winter underestimates) are from an excessive (insufficient) number of strong events (>20 mm day−1). Overall, GSMaP’s performance is comparable to other satellite-based products, with slightly better probability of detection during summer, and the different satellite-based estimates as a group have better agreement among themselves during summer than during winter.

Full access