Measuring Precipitation with Gauge, Radar and Satellites over U.S.: Where Are We? Christa D. Peters-Lidard, Yudong Tian, Code 614.3, NASA GSFC Nine high-resolution precipitation datasets were compared over the continental U.S. for a recent period of two years, to assess our current skill in measuring precipitation over the U.S.. These gridded datasets include two surface gauge analysis, one surface radar analysis, and six satellite- based estimates. Overall, all the products captured large-scale precipitation events successfully, especially over the eastern U.S. The two gauge analysis datasets (CPC-UNI and CPC) have the smallest differences from each other, being derived from nearly the same gauge networks but with different gridding algorithms. The surface radar analysis (STIV) is much closer to the gauge datasets, though notable differences remain over rough terrains in the western U.S. Satellite-based products have the largest differences from the other two categories, and among themselves. Their errors relative to the gauge analysis vary similarly with season and region, with better performances in summer Figure 2: The total biases in satellite-based estimates can be separated into 3 components: hit bias (red curve), missed precipitation (turquoise shade) , and false precipitation (orange shade). The time series of the 3 components, as well as the total biases (black curve) for 4 satellite-based datasets are shown for the western U.S. CPC gauge analysis was used as drospheric and Biospheric Sciences Laboratory Figure 1: Total biases (mm) in each of the 9 datasets for the summer of 2006 (JJA). CPC-UNI and CPC are based on gauge, STIV is based on radar. GSMaP (JAXA), 3B42RT (NASA/GSFC), CMORPH (NOAA/CPC), PERS (UCI) and NRL are all satelllite-based, and do not include gauge-based bias correction. 3B42, or TMPA includes gauge-based bias correction.
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Measuring Precipitation with Gauge, Radar and Satellites over U.S.: Where Are We?
Christa D. Peters-Lidard, Yudong Tian, Code 614.3, NASA GSFC
Nine high-resolution precipitation datasets were compared over the continental U.S. for a recent period of two years, to assess our current skill in measuring precipitation over the U.S.. These gridded datasets include two surface gauge analysis, one surface radar analysis, and six satellite-based estimates.
Overall, all the products captured large-scale precipitation events successfully, especially over the eastern U.S. The two gauge analysis datasets (CPC-UNI and CPC) have the smallest differences from each other, being derived from nearly the same gauge networks but with different gridding algorithms.
The surface radar analysis (STIV) is much closer to the gauge datasets, though notable differences remain over rough terrains in the western U.S. Satellite-based products have the largest differences from the other two categories, and among themselves. Their errors relative to the gauge analysis vary similarly with season and region, with better performances in summer than in winter, and over the eastern than over the western U.S.
Comparisons between TRMM 3B42 and 3B42RT illustrate the large impact of gauge-based bias correction.
Figure 2: The total biases in satellite-based estimates can be separated into 3 components: hit bias (red curve), missed precipitation (turquoise shade) , and false precipitation (orange shade). The time series of the 3 components, as well as the total biases (black curve) for 4 satellite-based datasets are shown for the western U.S. CPC gauge analysis was used as reference data.Hydrospheric and Biospheric Sciences Laboratory
Figure 1: Total biases (mm) in each of the 9 datasets for the summer of 2006 (JJA). CPC-UNI and CPC are based on gauge, STIV is based on radar. GSMaP (JAXA), 3B42RT (NASA/GSFC), CMORPH (NOAA/CPC), PERS (UCI) and NRL are all satelllite-based, and do not include gauge-based bias correction. 3B42, or TMPA includes gauge-based bias correction.
References:Tian, Y., C. D. Peters-Lidard, R. F. Adler, T. Kubota, and T. Ushio (2009), Evaluation of GSMaP precipitation estimates over contiguous U.S. J. Hydrometeor., in press.
Tian, Y., C. Peters-Lidard, J. Eylander, R. Joyce, G. Huffman, R. Adler, K.-L. Hsu, F. J. Turk, M. Garcia, and J. Zeng (2009), Component analysis of errors in satellite-based precipitation estimates, J. Geophys. Res., 114, D24101, DOI:10.1029/2009JD011949.
Tian, Y., C. D. Peters-Lidard, B. J. Choudhury and M. Garcia (2007), Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications, J. Hydrometeor., 8, 1165-1183.
Data Sources: NOAA CPC Unified Daily Gauge Analysis; NOAA CPC near-real-time daily precipitation analysis; NOAA NCEP Stage IV data; Air Force Weather Agency's Agricultural Meteorology modeling system; Global Satellite Mapping of Precipitation (GSMaP MVK+ Version 4.8.4); TRMM Multi-satellite Precipitation Analysis research product 3B42 Version 6; TRMM Multi-satellite Precipitation Analysis Real-time experimental product 3B42RT; NOAA Climate Prediction Center (CPC) MORPHing technique; Precipitation Estimation from Remotely Sensed Information using Artificial Neural NetworksNaval Research Laboratory's blended technique; Oregon State University Parameter-elevation Regressions on Independent Slopes Model (PRISM).
Technical Description of Figures:Figure 1: Total seasonal biases (mm) in each of the 9 datasets, compared to the monthly PRISM data, for the summer of 2006. It shows that the ground-based measurements (gauge and radar data) have rather small biases for summer. However, satellite-based measurements tend to have high overestimates over the central U.S., except for TMPA 3B42.
Figure 2: The total biases in satellite-based estimates can be separated into 3 components: hit bias (red curve), missed precipitation (turquoise shade) , and false precipitation (orange shade). The time series of the 3 components, as well as the total biases (black curve) for 4 sample satellite-based products are shown for the western CONUS. CPC gauge analysis was used as reference data.
Scientific significance: Our results provides a wide-angle assessment of our current skills in measuring precipitation over U.S. The results show that there are considerable uncertainties among these disparate measurements. Surface-based measurements and space-based measurements have a perceivable performance gap, and the uncertainties among the satellite products are exacerbated in winter when they are of the same order of magnitude as the data themselves. Together these products have at least twice as much uncertainty in winter as in summer. In the western U.S., it is particularly challenging for both surface-based and satellite-based measurements, as manifested by larger biases, lower probability of detection and higher false alarm rates.
Relevance for future science and relationship to Decadal Survey: Precipitation is one of the most difficult atmospheric variables to measure, but also one of the most critical variables for studies in climate change, water resources, hydrology and agriculture. Though rain gauges have been used for centuries, ground-based radar and space-borne sensors for decades, accurate measurement of precipitation is still a challenge. This challenge is what motivates the Tropical Rainfall Measurement Mission (TRMM) and the upcoming Global Precipitation Measurement (GPM) Mission.
Hydrospheric and Biospheric Sciences Laboratory
Persistent englacial drainage features in the Greenland Ice SheetPersistent englacial drainage features in the Greenland Ice SheetThomas Neumann, Code 614.1, NASA GSFCThomas Neumann, Code 614.1, NASA GSFC
Surface melting on the Greenland Ice Sheet during the brief summer is common up to ~1400 m elevation and, in extreme melt years, even higher. Water produced on the ice sheet surface collects in lakes and drains over the ice sheet surface in streams and down into the ice sheet through vertical conduits called moulins. Water delivered to the base of the ice sheet can cause uplift and enhanced sliding locally; this process was identified by the 4th IPCC report as a potentially de-stabilizing process for the Greenland Ice Sheet.
We used ice-penetrating radar data to observe the effects of significant melting of basal ice coincident with moulins and calculate how much basal melt occurred. We find more melting than can be explained by the release of potential energy from the drainage of surface meltwater in a single melt season, suggesting these moulins exist for multiple years. Observations indicate that once established, these moulins may be capable of establishing well-connected drainage pathways.
Figure 1: Location of radar profiles inWestern Greenland, near Swiss Camp.
Figure 2: Radar transect from A to A’ in Fig. 1. Internal layers are evident; basal melting distorts internal layers coincident with moulin at 8 km.Hydrospheric and Biospheric Sciences Laboratory
Name: Thomas Neumann, NASA/GSFC E-mail: Thomas.Neumann, [email protected]: 301-614-5923
References:
Catania, G.A. and T. A. Neumann. Persistent englacial drainage features in the Greenland Ice Sheet. Geophysical Research Letters, Vol 37, L02501, 2010.
Data Sources: This project was funded by NASA Cryospheric Science program, and was conducted in Western Greenland. Radar data were collected using a custom-built, low-frequency, short-pulse, ground-based radar system to image deep internal layers and the ice-bed interface. The transmitter was designed by Kentech Instruments and produces a 2kV output pulse with a repetition frequency of 1 kHz. The center frequency of the system is determined by the length of the resistively-loaded dipolar antennas. The data shown here used 2 MHz antennas with ~20m half-lengths.
Technical Description of Images:Figure 1: Background image is Landsat ETM image from 1 August 2001 in the vicinity of Swiss Camp, Western Greenland (inset map) showing bed elevation (thin grey contours), surface elevation (thick grey contours) [Bamber et al., 2001], moulin locations (white circles), moulins showing significant basal melt (black circles), supra-glacial lakes (dashed squares) and radar profiles. White lines indicate the location of radar data (A to A’) shown in Figure 2.
Figure 2: Radar profile along line A to A’, using 2 MHz radar set-up. Internal layers are lines of constant age in the ice sheet and are approximately conformal with the bedrock (very bright reflector at variable depth indicating ice thickness of ~600m above sea level between ~6 and ~10km, and thicker elsewhere). Vertically-stacked reflection hyperbolae indicate likely presence of drainage pathway (moulin) at ~8km. Down-warping of internal layers around 8km indicates removal of basal ice and associated truncation of internal layers.
Scientific significance: Our work indicates that only a few persistent moulins (2 of the 31 moulins imaged in our radar data) in our study area drain the equivalent of multiple lakes per year and likely remain active over several years. Observations indicate that once established, these persistent moulins might be capable of establishing well-connected melt water drainage pathways. This finding also suggests that the effects of seasonal lake drainages on ice flow are localized, and may not have a major impact on ice sheet stability, contrary to suggestions in the 4 th IPCC Assessment Report.
Relevance for future science and relationship to Decadal Survey: The Decadal Survey identifies ice sheet mass balance, and mass balance changes, as key issues in Climate Variability and Change. Our project provides guidance for large scale ice modeling by indicating that the effects of meltwater drainage may be localized, and that only a few moulins seem to exist for several years and lead to significant basal melting.
Hydrospheric and Biospheric Sciences Laboratory
Assessing the Coupling between Surface Albedo derived from MODISAssessing the Coupling between Surface Albedo derived from MODISand the Fraction of Diffuse Skylight over NSA-Barrowand the Fraction of Diffuse Skylight over NSA-Barrow
Miguel O. Román, Code 614.5 NASA/GSFC
Land surface albedo is a key parameter in radiation budget and climate modeling studies. When using linear kernel-driven BRDF models to estimate albedo, it is usual to consider the diffuse illumination field isotropic. However, when the solar zenith angle exceeds around 70 and/or for optically thick atmospheres, an isotropic assumption can lead to quite large errors.
A new method has been implemented to retrieve instantaneous albedo from MODIS, including multiple scattering effects and the directional distribution of sky radiance. This method was tested using MODTRAN®5.1 and validated against coincident field (or tower) measurements at NSA-Barrow.
Asian Dust Event Asian Dust Event
Figure 1: Total sky imager
Figure 2: NSA – Barrow: 71° 19' 22.73" N, 156° 36' 56.70" W
Figure 3: Time-Series AnalysisHydrospheric and Biospheric Sciences Laboratory
Name: Miguel O. Román, NASA/GSFC E-mail: [email protected] Phone: 301-614-5498
References:
- Román, M.O., C.B. Schaaf, P. Lewis, F. Gao, G.P. Anderson, J.L. Privette, A.H. Strahler, C.E. Woodcock, and M. Barnsley (2010). Assessing the Coupling between Surface Albedo derived from MODIS and the Fraction of Diffuse Skylight over Spatially-Characterized Landscapes. Remote Sensing of Environment, 114, 738-760, 10.1016/j.rse.2009.11.014.
- Holben, B.N., Tanre, D., Smirnov, A., Eck, T.F., Slutsker, I., N., A., Newcomb, W.W., Schafer, J., Chatenet, B., Lavenue, F., Kaufman, Y.J., J., V.-C., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karnieli, A., O'Neill, N.T., Pietras, C., Pinker, R.T., Voss, K., & Zibordi, G. (2001). An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET. Journal of Geophysical Research, 106, 12067-12097.
- Liu, J., C.B. Schaaf, A.H. Strahler, Z. Jiao, Y. Shuai, Q. Zhang, M.O. Román, J.A. Augustine, and E.G. Dutton (2009). Validation of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo retrieval algorithm: Dependence of albedo on solar zenith angle J. Geophys. Res., 114(D01106), 10.1029/2008JD009969.
-Stone, R.S., G.P. Anderson, E.P. Shettle, E. Andrews, K. Loukachine, E.G. Dutton, C. Schaaf, and M.O. Román (2008). Radiative impact of boreal smoke in the Arctic: Observed and modeled J. Geophys. Res., 113(D14S16), 10.1029/2007JD009657.
-Data Sources: This is a joint effort composed of multiple institutes including the DoE-ARM Climate Research Facility, NASA-GSFC’s Land Product Validation Team, and Boston University’s Center for Remote Sensing. Data from the NSA-Barrow station is available at the Clouds and the Earth’s Radiant Energy System (CERES) ARM Validation Experiment (CAVE) archive (http://snowdog.larc.nasa.gov/cave/).
Technical Description of Image:
Figure 1: The total sky imager (TSI) provides time series of hemispheric sky images during daylight hours and retrievals of fractional sky cover and aerosol optical depth (AOD) for periods when the solar elevation is greater than 10 degrees. Source ARM-DoE.
Figure 2: Field station at Barrow, Alaska. Image courtesy of NOAA/ESRL/GMD.
Figure 3: Diurnal change in surface albedo (SW – 0.3-5.0 um) from tower measurements and MODIS retrievals (using both isotropic and full expressions) during DOY 89-114, 2002 at the NSA-Barrow measurement site. (Right-axis) Coincident retrievals of aerosol optical depth at 550nm from the local AERONET sunphotometer (Holben et al. 2001).Scientific significance: Regional surface albedos (<1.0km) with an absolute accuracy of 0.05 units (Henderson-Sellers and Wilson 1983; Sellers et al. 1995) for snow-covered lands are required by climate, biogeochemical, hydrological, and weather forecast models. Explicit characterization of the influence of anisotropic diffuse illumination and multiple scattering improves the accuracy of MODIS snow albedo retrievals (relative biases with bounds ranging from ~20-25%) by a factor of 1.5-2x enabling MODIS snow albedo to meet the absolute accuracy requirement of 0.05 units.
Relevance for future science and relationship to Decadal Survey: As production moves from EOS-MODIS data into NPP-VIIRS and future land science products, user specifications require that production of global surface albedo also moves from the current multi-date approach to daily albedo computations. Thus, directional observations acquired during a single day overpass must dominate the retrieval through coupling with daily rolling surface reflectance anisotropy retrievals that provide crucial a priori knowledge of the underlying surface. These new methods need to be examined to ensure that the new algorithms are holding on throughout periods of rapidly changing events (e.g. snow-melt, inundation, burning, clearing, and tilling).
