Junjun Hu

Evaluation and Uncertainty Estimation of NOAA/NSSL Next-Generation National Mosaic Quantitative Precipitation Estimation Product (Q2) over the Continental United States

AbstractQuantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential Z–R equations has been successful in removing regional biases (i.e., overestimated ...

Evaluation of High-Resolution Precipitation Estimates from Satellites during July 2012 Beijing Flood Event Using Dense Rain Gauge Observations

Satellite-based precipitation estimates products, CMORPH and PERSIANN-CCS, were evaluated with a dense rain gauge network over Beijing and adjacent regions for an extremely heavy precipitation event on July 21 2012. CMORPH and PEERSIANN-CSS misplaced the region of greatest rainfall accumulation, and failed to capture the spatial pattern of precipitation, evidenced by a low spatial correlation coefficient (CC). CMORPH overestimated the daily accumulated rainfall by 22.84% while PERSIANN-CCS underestimated by 72.75%. In the rainfall center, both CMORPH and PERSIANN-CCS failed to capture the temporal variation of the rainfall, and underestimated rainfall amounts by 43.43% and 87.26%, respectively. Based on our results, caution should be exercised when using CMORPH and PERSIANN-CCS as input for monitoring and forecasting floods in Beijing urban areas, and the potential for landslides in the mountainous zones west and north of Beijing.

Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS

AbstractIn this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agencys (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratorys (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering...

Understanding the Changing Characteristics of Droughts in Sudan and the Corresponding Components of the Hydrologic Cycle

Droughts are becoming the most expensive natural disasters in former Sudan and have exerted serious impacts on local economic development and ecological environment. The purpose of this paper is to improve understanding of the temporal and spatial variations of droughts by using the Standard Precipitation Index (SPI) and to discuss their relevance to the changes of hydrological variables in Sudan. The analysis results show that 1) droughts start in the late 1960s in Sudan and severe droughts occur during the 1980s in different regions of Sudan—the annual precipitation and soil moisture also reveal the evidence that the droughts prevail since the late 1960s; 2) the greater negative soil moistures anomalies are found in central and southern Sudan during the rainy seasons while greater negative anomalies of precipitation occur only in central Sudan compared between 1969‐2009 and 1948‐68; 3) the precipitation recycling ratio averaged over 1948‐2009 decreases from south to north and the percentage of local actual evapotranspiration to local precipitation in dry conditions is greater than that in wet conditions; and 4) the highest (second highest) correlations appear between soil moisture and precipitation (actual evapotranspiration) and the significant decreases in annual soil moisture are associated with the decrease of annual precipitation and the increase of annual temperature. This suggests that continuous droughts in Sudan are caused jointly by the decrease of precipitation and the increase of temperature in the region.

Mapping the Precipitation Type Distribution Over the Contiguous United States Using NOAA/NSSL National Multi-Sensor Mosaic QPE

Understanding the Earths energy cycle and water balance requires an understanding of the distribution of precipitation types and their total equivalent water budget estimation. The fine distribution of precipitation types over the contiguous United States (CONUS) is not yet well understood due to either unavailability or coarse resolution of previous satellite- and ground radar-based precipitation products that have difficulty in classifying precipitation. The newly available NOAA/National Severe Storms Laboratory ground radar network-based National Multi-Sensor Mosaic QPE (NMQ/Q2) System has provided precipitation rates and types at unprecedented high spatiotemporal resolution. Here, four years of 1 km/5 min observations derived from the NMQ are used to probe spatiotemporal distribution and characteristics of precipitation types (stratiform, convective, snow, tropical/warm (T/W), and hail) over CONUS, resulting in assessment of occurrence and volume contribution for these precipitation types through the four-year period, including seasonal distributions, with some radar coverage artifacts. These maps in general highlight the snow distribution over northwestern and northern CONUS, convective distribution over southwestern and central CONUS, hail distribution over central CONUS, and T/W distribution over southeastern CONUS. The total occurrences (contribution of total rain amount/volume) of these types are 72.88% (53.91%) for stratiform, 21.15% (7.64%) for snow, 2.95% (19.31%) for T/W, 2.77% (14.03%) for convective, and 0.24% (5.11%) for hail. This paper makes it possible to prototype a near seamless high-resolution reference for evaluating satellite swath-based precipitation type retrievals and also a potentially useful forcing database for energy-water balance budgeting and hydrological prediction for the United States.

Intercomparison of Precipitation Estimates From WSR-88D Radar and TRMM Measurement Over Continental United States

This paper examines the spatial error structures of precipitation estimates derived from both WSR-88D ground radar measurements and National Aeronautics and Space Administrations Tropical Rainfall Measurement Mission (TRMM) satellite-based radar and passive microwave measurements. The surface and spaceborne precipitation products are systematically evaluated via comparison with the Climate Prediction Center Unified Gauge Analysis over the Continental United States (CONUS) from December 2008 through November 2010. The WSR-88D quantitative precipitation estimation (QPE) products analyzed include the national mosaic daily QPE products (Q2) and Stage II and Stage IV daily products. The TRMM QPE products analyzed include the version-7 real-time product 3B42RT and the research product 3B42 (3B42V7). The results of the comparison based on two-year mean daily precipitation over CONUS demonstrate the following: 1)3B42V7 and Stage IV perform fairly similarly with correlation coefficients (CCs) of 0.92 and 0.91, relatively low (magnitude) relative biases (RBs) of -3.32% and -7.16%, and low root-mean-squared errors (RMSEs) of 0.49 and 0.54 mm/day, respectively; 2) the gauge-corrected daily Q2 product (Q2RadGC) is slightly inferior to the 3B42V7 and Stage IV products but outperforms both the real-time satellite-only product 3B42RT and the two radar-only products (Q2Rad and Stage II) in terms of both CC and RMSE; 3) Q2Rad shows similar performance to 3B42RT regarding RB and CC; and 4) Stage II has particularly poor performance and suffers from systematic precipitation overestimation, particularly in northeastern Oregon, northern Utah, northeastern Carolina, and northern Georgia.

Precipitation Spectra Analysis Over China With High-Resolution Measurements From Optimally-Merged Satellite/Gauge Observations—Part II: Diurnal Variability Analysis

Timing and diurnal variation of summer precipitation is analyzed over China using a new high-resolution (0.1°, hourly) satellite-gauge merged surface rainfall dataset that spans from 2008 through 2013. The results show that: 1) both precipitation amount (PA) and frequency (PF) show strong diurnal cycles with local solar time (LST); 2) peak times of PA (PAPT) primarily occur from 15 LST to 00 LST in most parts of the Tibet Plateau (TP), Xinjiang (XJ), Northwestern China (NW), Northeastern China (NE), and Southern China (SC), and the PAPT occurs from 00 LST to 09 LST in southern TP, Eastern XJ, western NW, southern NE, eastern Northern China (NC), and most parts of Southwestern China (SW); 3) the PAPT transitions eastward with time, occurring at ~15 LST in central TP, at midnight in SW, and at 15-18 LST in the eastern coastal regions that are in the lower reach of Yangtze River and in the north side of Wuyi Mountains; 4) peak times of PF (PFPT) show a similar spatial pattern with PAPT, but with a small temporal (1-2 h) lead; 5) peak times of precipitation intensity (PIPT) occur during the 18-00 LST time frame in the southeastern TP and central SW regions. The PIPT along the upper Yangtze River valley occurs around 00-06 LST. The PIPT occurs in the morning at around 06-09 LST in the mid-lower Yangtze River valley and most parts of SC. This study on the diurnal cycle of precipitation over China can be used as a reference to validate atmospheric and hydrologic models, and also to guide hydrometerological research and applications.

Precipitation Spectra Analysis Over China With High-Resolution Measurements From Optimally Merged Satellite/Gauge Observations—Part I: Spatial and Seasonal Analysis

Precipitation amount (PA), frequency (PF), and intensity (PI) over China are characterized and quantified using a high-resolution merged satellite-gauge precipitation product for 6 years (January 2008 through December 2013). The precipitation product synthesizes both state-of-the-art multisatellite precipitation algorithms and the latest, densest gauge observations to provide high-quality precipitation information at a very fine temporal and spatial resolution (0.1°/hourly) that encompasses all of China. The geographical and seasonal variations in precipitation are systematically documented over seven subregions, each corresponding to a unique climate regime. PA, PF, and PI have large seasonal and geographical variations across China. It is found that 1) although heavy precipitation events (>10 mm/h) represent only 0.8% of total precipitation occurrence over China, they contribute 12.1% of the total precipitation volume. Light precipitation events (<;1 mm/h) dominate the precipitation occurrence (74.3%) and contribute 23.1% of the total precipitation volume; 2) over the high-altitude Tibetan Plateau (TP), the land-locked Xinjiang (XJ) province, and northwestern China (NW), light precipitation events (<;1 mm/h) occur very frequently (74.7%, 82.1%, and 64.1% of all precipitation events) and contribute 29.8%, 35.5%, and 27.4% of the total precipitation volume. This initial continental-scale study provides new insights on precipitation characteristics that can benefit meteorological and hydrological modeling and applications, especially in areas with sparse rain-gauge coverage.

Restoration of 1-24 hour dry-bulb temperature gaps for use in building performance monitoring and analysis—Part I

Building energy system retrofit and retro-commissioning projects present tremendous opportunities to save energy. Energy consumption in buildings, especially HVAC systems, is significantly impacted by weather conditions. However, short- or long-term climatic data are frequently missing because of data transmission problems, data quality assurance methods, sensor malfunction, or a host of other reasons. These gaps in climatic data continue to provide challenges for HVAC engineers in monitoring and verifying building energy performance. This article examines eight classical approaches that use Linear interpolation, Lagrange interpolation, and Cubic Spline interpolation techniques, and eleven approaches that use two newly developed methods, i.e., Angle-based interpolation and Corr-based interpolation, to restore up to 24 h of missing dry-bulb temperature data in a time series for use in building performance monitoring and analysis. Eleven one-year hourly data sets are used to evaluate the performance of these 19 different methods. Each method is applied to deal with artificial gaps that are generated randomly. In terms of the difference between estimated values and measured values, two types of comparisons are carried out. The first comparison is conducted with three evaluation indices: MAE, RMSE, and STDBIAS. The second comparison is based on the percentage of the total data that can be estimated by an approach within specific error thresholds, including 1°F (0.56°C), 2°F (1.11°C), 3°F (1.67°C), and 5°F (2.78°C), from measured values. The comparison results show that Linear interpolation performs best when filling 1–2 h gaps, Lagrange interpolation (Lag2L2R) outperforms other methods when gaps are 3–8 h long, and the Corr-based interpolation method (Corr1L1R24Avg) is a better technique for filling 9–24 h gaps. This article presents the first part of the research results through the ASHRAE 1413 research project. The second part of the results focuses on methods to filling long-term dry-bulb temperature gaps.

Restoration of missing dry-bulb temperature data with long-term gaps (up to 60 days) for use in building performance monitoring and analysis—Part II

The lack of standard procedures for filling climatic data has the potential to undermine design, monitoring, and control efforts aimed at climate-responsive building design, performance monitoring, and energy efficiency. This article addresses the challenge of long-term missing gaps in dry-bulb temperature data by examining three spatial methods, namely the inverse distance weighting (IDW) method, the spatial regression test (SRT) method, and the substitution with best match data (SSBM) method, as well as two temporal methods, namely the temporal regression test (TRT) method and the temporal substitution with best match data (TSBM) method. Using these methods, missing dry-bulb temperature data with long-term gaps, ranging from 1 to 60 days, are restored for use in building performance monitoring and analysis. Three one-year, hourly datasets were used to evaluate the performance of these approaches. Each method was applied to deal with artificial gaps which were generated randomly and represented different seasons of a year. In terms of the difference between estimated values and measured values, three evaluation indices, namely mean absolute error (MAE), root mean square error (RMSE), and standard error of bias (BIASSTD), were utilized. The comparison results show that spatial methods are better than temporal methods. The confidence level of the SRT method was further investigated by applying this method to existing data and missing data, and examining its performance. The results indicate that the uncertainty of the SRT method can be predicted and at least two neighboring stations are recommended when using it. This is the second part of the research results obtained through the ASHRAE 1413 research project (in press) with a focus on introducing gap-filling methods for long-term gaps in dry-bulb temperature.