scholarly journals The Influence of Synoptic-Scale Air Mass Conditions on Seasonal Precipitation Patterns over North Carolina

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 624
Author(s):  
Christopher Zarzar ◽  
Jamie Dyer
Keyword(s):  
North Carolina ◽  
Stage Iv ◽  
Synoptic Scale ◽  
Spatial And Temporal Patterns ◽  
Air Mass ◽  
Synoptic Classification ◽  
Precipitation Estimates ◽  
Environmental Prediction ◽  
Heterogeneous Clustering ◽  
Rain Rates

This paper characterizes the influence of synoptic-scale air mass conditions on the spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003–2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the Coastal Plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions, whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, the highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003–2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina.

scholarly journals Influence of Synoptic-Scale Air Mass Conditions on Seasonal Precipitation Patterns over North Carolina

2019 ◽  
Author(s):  
Christopher Zarzar ◽  
Jamie Dyer
Keyword(s):  
North Carolina ◽  
Stage Iv ◽  
Synoptic Scale ◽  
Spatial And Temporal Patterns ◽  
Air Mass ◽  
Synoptic Classification ◽  
Precipitation Estimates ◽  
Environmental Prediction ◽  
Heterogeneous Clustering ◽  
Rain Rates

This paper characterizes the influence of synoptic-scale air mass conditions on spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003-2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the coastal plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003-2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina.

scholarly journals Influence of Synoptic-Scale Airmass Conditions on Seasonal Precipitation Patterns over North Carolina

2019 ◽  
Author(s):  
Christopher Zarzar ◽  
Jamie Dyer
Keyword(s):  
North Carolina ◽  
Stage Iv ◽  
Synoptic Scale ◽  
Spatial And Temporal Patterns ◽  
Synoptic Classification ◽  
Daily Average ◽  
Average Precipitation ◽  
Precipitation Estimates ◽  
Environmental Prediction ◽  
Clear Shift

This paper characterizes the influence of synoptic-scale airmass conditions on spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003-2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing airmass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily average precipitation amounts distributed along the lee side of the Appalachian Mountains and along the coastal plains. Significant and heterogenous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. Between the three geographic regions of North Carolina, highest precipitations amounts were received in western North Carolina during the winter and spring, but this signal shifted to eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing airmass conditions. The summer months were dominated by maritime tropical airmass conditions with no clear shift in summertime airmass trends over the study period. Most days with recorded precipitation in the winter, spring, and fall occurred under dry moderate airmass conditions; however, the highest daily average precipitation and total precipitation occurred under the influence of maritime moderate airmasses. Importantly, there was an observed shift toward warmer and more humid airmass conditions in the winter, spring, and fall months throughout the study period (2003-2018), indicating a shift toward airmass conditions conducive to higher daily average rain rates in North Carolina.

scholarly journals Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

2015 ◽  
Vol 16 (2) ◽  
pp. 811-829 ◽  
Author(s):  
Liao-Fan Lin ◽  
Ardeshir M. Ebtehaj ◽  
Rafael L. Bras ◽  
Alejandro N. Flores ◽  
Jingfeng Wang
Keyword(s):  
Data Assimilation ◽  
Spatial Resolution ◽  
Numerical Experiments ◽  
Wrf Model ◽  
Stage Iv ◽  
Small Scale ◽  
Point Scale ◽  
Precipitation Estimates ◽  
Environmental Prediction ◽  
The Impact

Abstract The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

scholarly journals Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002–2012)

2014 ◽  
Vol 11 (10) ◽  
pp. 11489-11531 ◽  
Author(s):  
O. P. Prat ◽  
B. R. Nelson
Keyword(s):  
Real Time ◽  
Stage Iv ◽  
Rain Gauge ◽  
Contingency Analysis ◽  
Western Us ◽  
Precipitation Estimates ◽  
Satellite Radar ◽  
Quantitative Precipitation Estimates ◽  
Rain Rates ◽  
Surface Observations

Abstract. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002–2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (−33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

scholarly journals Assessment and Implications of NCEP Stage IV Quantitative Precipitation Estimates for Product Intercomparisons

2016 ◽  
Vol 31 (2) ◽  
pp. 371-394 ◽  
Author(s):  
Brian R. Nelson ◽  
Olivier P. Prat ◽  
D.-J. Seo ◽  
Emad Habib
Keyword(s):  
Cumulative Distribution Function ◽  
Daily Precipitation ◽  
Processing System ◽  
Stage Iv ◽  
Cumulative Distribution ◽  
Precipitation Estimates ◽  
Daily Scale ◽  
Processing Algorithms ◽  
Environmental Prediction ◽  
Quantitative Precipitation Estimates

Abstract The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are used in many studies for intercomparisons including those for satellite QPEs. An overview of the National Weather Service precipitation processing system is provided here so as to set the stage IV product in context and to provide users with some knowledge as to how it is developed. Then, an assessment of the stage IV product over the period 2002–12 is provided. The assessment shows that the stage IV product can be useful for conditional comparisons of moderate-to-heavy rainfall for select seasons and locations. When evaluating the product at the daily scale, there are many discontinuities due to the operational processing at the radar site as well as discontinuities due to the merging of data from different River Forecast Centers (RFCs) that use much different processing algorithms for generating their precipitation estimates. An assessment of the daily precipitation estimates is provided based on the cumulative distribution function for all of the daily estimates for each RFC by season. In addition it is found that the hourly estimates at certain RFCs suffer from lack of manual quality control and caution should be used.

scholarly journals Improvements to the GOES-R Rainfall Rate Algorithm

2016 ◽  
Vol 17 (6) ◽  
pp. 1693-1704 ◽  
Author(s):  
Robert J. Kuligowski ◽  
Yaping Li ◽  
Yan Hao ◽  
Yu Zhang
Keyword(s):  
Stage Iv ◽  
Rainfall Rate ◽  
Training Data ◽  
Validation Data ◽  
Current Generation ◽  
Spectral Bands ◽  
Ground Validation ◽  
Environmental Prediction ◽  
Rain Rates ◽  
Selection Of

Abstract The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar–gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%.

scholarly journals Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 687
Author(s):  
Salman Sakib ◽  
Dawit Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
Stage Iv ◽  
Tropical Storm ◽  
Average Correlation ◽  
Acceptable Range ◽  
Precipitation Estimates ◽  
Overall Performance ◽  
Early Late ◽  
Statistical Metrics ◽  
Better Than

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

scholarly journals A Multiscale Evaluation of Multisensor Quantitative Precipitation Estimates in the Russian River Basin

2019 ◽  
Vol 20 (3) ◽  
pp. 447-466 ◽  
Author(s):  
Janice L. Bytheway ◽  
Mimi Hughes ◽  
Kelly Mahoney ◽  
Robert Cifelli
Keyword(s):  
Reanalysis Data ◽  
Small Scale ◽  
Synoptic Scale ◽  
Unique Challenge ◽  
Basin Scale ◽  
Variable Nature ◽  
Precipitation Estimates ◽  
Russian River ◽  
Quantitative Precipitation Estimates ◽  
Precipitation Events

Abstract The Russian River in northern California is an important hydrological resource that typically depends on a few significant precipitation events per year, often associated with atmospheric rivers (ARs), to maintain its annual water supply. Because of the highly variable nature of annual precipitation in the region, accurate quantitative precipitation estimates (QPEs) are necessary to drive hydrologic models and inform water management decisions. The basin’s location and complex terrain present a unique challenge to QPEs, with sparse in situ observations and mountains that inhibit remote sensing by ground radars. Gridded multisensor QPE datasets can fill in the gaps but are susceptible to both the errors and uncertainties from the ingested datasets and uncertainties due to interpolation methods. In this study a dense network of independently operated rain gauges is used to evaluate gridded QPE from the Multi-Radar Multi-Sensor (MRMS) during 44 precipitation events occurring during the 2015/16 and 2016/17 wet seasons (October–March). The MRMS QPE products matched the gauge estimates of precipitation reasonably well in approximately half the cases but failed to capture the spatial distribution and intensity of the rainfall in the remaining cases. ERA-Interim reanalysis data suggest that the differences in performance are related to synoptic-scale patterns and AR landfall location. These synoptic-scale differences produce different rainfall distributions and influence basin-scale winds, potentially creating regions of small-scale precipitation enhancement or suppression. Data from four profiling radars indicated that a larger fraction of the precipitation in poorly captured events occurred as shallow stratiform rain unobserved by radar.

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