The Diurnal Cycle of Clouds and Precipitation along the Sierra Madre Occidental Observed during NAME-2004: Implications for Warm Season Precipitation Estimation in Complex Terrain

2008 ◽  
Vol 9 (4) ◽  
pp. 728-743 ◽  
Author(s):  
Stephen W. Nesbitt ◽  
David J. Gochis ◽  
Timothy J. Lang
Keyword(s):  
High Resolution ◽  
Diurnal Cycle ◽  
Complex Terrain ◽  
Deep Convection ◽  
Sierra Madre Occidental ◽  
Convective Systems ◽  
Sierra Madre ◽  
Precipitation Estimation ◽  
Rainfall Estimates ◽  
Tier I

Abstract This study examines the spatial and temporal variability in the diurnal cycle of clouds and precipitation tied to topography within the North American Monsoon Experiment (NAME) tier-I domain during the 2004 NAME enhanced observing period (EOP, July–August), with a focus on the implications for high-resolution precipitation estimation within the core of the monsoon. Ground-based precipitation retrievals from the NAME Event Rain Gauge Network (NERN) and Colorado State University–National Center for Atmospheric Research (CSU–NCAR) version 2 radar composites over the southern NAME tier-I domain are compared with satellite rainfall estimates from the NOAA Climate Prediction Center Morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) operational and Tropical Rainfall Measuring Mission (TRMM) 3B42 research satellite estimates along the western slopes of the Sierra Madre Occidental (SMO). The rainfall estimates are examined alongside hourly images of high-resolution Geostationary Operational Environmental Satellite (GOES) 11-μm brightness temperatures. An abrupt shallow to deep convective transition is found over the SMO, with the development of shallow convective systems just before noon on average over the SMO high peaks, with deep convection not developing until after 1500 local time on the SMO western slopes. This transition is shown to be contemporaneous with a relative underestimation (overestimation) of precipitation during the period of shallow (deep) convection from both IR and microwave precipitation algorithms due to changes in the depth and vigor of shallow clouds and mixed-phase cloud depths. This characteristic life cycle in cloud structure and microphysics has important implications for ice-scattering microwave and infrared precipitation estimates, and thus hydrological applications using high-resolution precipitation data, as well as the study of the dynamics of convective systems in complex terrain.

scholarly journals Influence of Spatial Resolution on Diurnal Variability during the North American Monsoon

2008 ◽  
Vol 21 (16) ◽  
pp. 3967-3988 ◽  
Author(s):  
J. Li ◽  
S. Sorooshian ◽  
W. Higgins ◽  
X. Gao ◽  
B. Imam ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Diurnal Cycle ◽  
Gulf Of California ◽  
The West ◽  
Diurnal Variability ◽  
Sierra Madre Occidental ◽  
Sierra Madre ◽  
Low Level Jet ◽  
Northwestern Mexico

Abstract Diurnal variability is an important yet poorly understood aspect of the warm-season precipitation regime over southwestern North America. In an effort to improve its understanding, diurnal variability is investigated numerically using the fifth-generation Pennsylvania State University (PSU)–NCAR Mesoscale Model (MM5). The goal herein is to determine the possible influence of spatial resolution on the diurnal cycle. The model is initialized every 48 h using the operational NCEP Eta Model 212 grid (40 km) model analysis. Model simulations are carried out at horizontal resolutions of both 9 and 3 km. Overall, the model reproduces the basic features of the diurnal cycle of rainfall over the core monsoon region of northwestern Mexico and the southwestern United States. In particular, the model captures the diurnal amplitude and phase, with heavier rainfall at high elevations along the Sierra Madre Occidental in the early afternoon that shifts to lower elevations along the west slopes in the evening. A comparison to observations (gauge and radar data) shows that the high-resolution (3 km) model generates better rainfall distributions on time scales from monthly to hourly than the coarse-resolution (9 km) model, especially along the west slopes of the Sierra Madre Occidental. The model has difficulty with nighttime rainfall along the slopes, over the Gulf of California, and over Arizona. A comparison of surface wind data from three NCAR Integrated Sounding System (ISS) stations and the Quick Scatterometer (QuikSCAT) to the model reveals a low bias in the strength of the Gulf of California low-level jet, even at high resolution. The model results indicate that outflow from convection over northwestern Mexico can modulate the low-level jet, though the extent to which these relationships occur in nature was not investigated.

scholarly journals Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

2013 ◽  
Vol 14 (5) ◽  
pp. 1500-1514 ◽  
Author(s):  
Dimitrios Stampoulis ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos
Keyword(s):  
High Resolution ◽  
Geographical Area ◽  
Heavy Precipitation ◽  
Type Systems ◽  
Economic Losses ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Precipitation Estimation ◽  
Rainfall Estimates ◽  
Precipitation Events

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.

Correction of Radar QPE Errors for Non-Uniform VPRs Using TRMM Observations

2013 ◽  
Vol 726-731 ◽  
pp. 3391-3396
Author(s):  
Man Zhang ◽  
You Cun Qi
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Tropical Rainfall Measuring Mission ◽  
Cool Season ◽  
Convective Systems ◽  
Freezing Level ◽  
Stratiform Region ◽  
Stratiform Precipitation ◽  
Precipitation Estimation ◽  
Mesoscale Convective

Mesoscale Convective Systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) or equivalent high-reflectivity region is often found in the stratiform precipitation. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE), and a vertical profile of reflectivity (VPR) correction is necessary to reduce the error. VPR corrections of the radar QPE is more difficult for MCSs than for a widespread cool season stratiform precipitation because of the spatial non-homogeneity of MCSs. Further, microphysical processes in the MCS stratiform region are more complicated than in the large-scale cool season stratiform precipitation. A clearly defined BB bottom, which is critical for accurate VPR corrections, is often not found in ground radar VPRs from MCSs. This is a big challenge when the stratiform region of MCSs is far away from the radar where the radar beam is too high or too wide to resolve the BB bottom. Further, variations of reflectivity below the freezing level are much more significant in MCSs than in a large-scale cool season precipitation, requiring high-resolution radar observations near the ground for an effective VPR correction. The current study seeks to use the vertical precipitation structure observed from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) to aid VPR corrections of the ground radar QPE in MCSs. High-resolution VPRs are derived from TRMM data for MCSs and then applied for the correction of ground radar QPEs.

scholarly journals A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

2021 ◽  
Vol 13 (2) ◽  
pp. 827-856
Author(s):  
Jianfeng Li ◽  
Zhe Feng ◽  
Yun Qian ◽  
L. Ruby Leung
Keyword(s):  
United States ◽  
High Resolution ◽  
Atmospheric Chemistry ◽  
Rocky Mountains ◽  
Deep Convection ◽  
The United States ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Data Product ◽  
Mesoscale Convective

Abstract. Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated Flexible Object Tracker algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by season, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems; quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events; and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at https://doi.org/10.25584/1632005 (Li et al., 2020).

scholarly journals A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

2020 ◽  
Author(s):  
Jianfeng Li ◽  
Zhe Feng ◽  
Yun Qian ◽  
L. Ruby Leung
Keyword(s):  
United States ◽  
High Resolution ◽  
Atmospheric Chemistry ◽  
Rocky Mountains ◽  
Deep Convection ◽  
The United States ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Data Product ◽  
Mesoscale Convective

Abstract. Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated FLEXTRKR (Flexible Object Tracker) algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by seasons, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems, quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events, and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at https://doi.org/10.25584/1632005 (Li et al., 2020).

scholarly journals Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

2018 ◽  
Vol 10 (8) ◽  
pp. 1258 ◽  
Author(s):  
Marios Anagnostou ◽  
Efthymios Nikolopoulos ◽  
John Kalogiros ◽  
Emmanouil Anagnostou ◽  
Francesco Marra ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Rain Gauge ◽  
Storm Events ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Radar Observations ◽  
X Band ◽  
Precipitation Estimation ◽  
Rainfall Estimates

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

scholarly journals Diagnosing the average spatio-temporal impact of convective systems – Part 2: A model inter-comparison using satellite data

2014 ◽  
Vol 14 (7) ◽  
pp. 9155-9201 ◽  
Author(s):  
M. S. Johnston ◽  
S. Eliasson ◽  
P. Eriksson ◽  
R. M. Forbes ◽  
A. Gettelman ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Rain Rate ◽  
Climate Models ◽  
Deep Convection ◽  
Geographical Location ◽  
Longwave Radiation ◽  
Convective Systems ◽  
Deep Convective Systems ◽  
Spatio Temporal ◽  
Ice Water

Abstract. The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation (OLR) is evaluated in the climate models EC-Earth, ECHAM6, and CAM5 using satellite observations. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate (RR) maxima in order to focus on the temporal evolution of the deep convective processes in the model and observations. The models tend to over-produce rain rates less than about 3 mm h−1 and underpredict the occurrence of more intense rain. While the diurnal distribution of oceanic rain rate maxima in the models is similar to the observations, the land-based maxima are out of phase. Over land, the diurnal cycle of rain is too intense, with DC events occurring at the same position on subsequent days, while the observations vary more in timing and geographical location. Despite having a larger climatological mean upper tropospheric relative humidity, models closely capture the observed moistening of the upper troposphere following the peak rain rate in the deep convective systems. A comparison of the evolution of vertical profiles of ice water content and cloud fraction shows significant differences between models and with the observations. Simulated cloud fractions near the tropopause are also larger than observed, but the corresponding ice water contents are smaller compared to the observations. EC-Earth's CF at pressure levels > 300 hPa are generally less than the obervations while the other models tend to have larger CF for similar altitudes. The models' performance for ocean-based systems seems to capture the evolution of DC systems fairly well, but the land-based systems show significant discrepancies. In particular, the models have a significantly stronger diurnal cycle at the same geo-spatial position. Finally, OLR anomalies associated with deep convection are in reasonable agreement with the observations. This study shows that such agreement with observations can be achieved in different ways in the three models due to different representations of deep convection processes and compensating errors.

scholarly journals Exploring the potential of utilizing high resolution X-band radar for urban rainfall estimation

2016 ◽  
Author(s):  
Wen-Yu Yang ◽  
Guang-Heng Ni ◽  
You-Cun Qi ◽  
Yang Hong ◽  
Ting Sun
Keyword(s):  
High Resolution ◽  
Rainfall Estimation ◽  
Wind Drift ◽  
Common Error ◽  
Error Corrections ◽  
Raindrop Size Distribution ◽  
X Band ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Rainfall Estimates

Abstract. X-band-radar-based quantitative precipitation estimation (QPE) system is increasingly gaining interest thanks to its strength in providing high spatial resolution rainfall information for urban hydrological applications. However, prior to such applications, a variety of errors associated with X-band radars are mandatory to be corrected. In general, X-band radar QPE systems are affected by two types of errors: 1) common errors (e.g. mis-calibration, beam blockage, attenuation, non-precipitation clutter, variations in the raindrop size distribution) and 2) “wind drift” errors resulting from non-vertical falling of raindrops. In this study, we first assess the impacts of different corrections of common error using a dataset consisting of one-year reflectivity observations collected at an X-band radar site and a distrometer along with rainfall observations in Beijing urban area. The common error corrections demonstrate promising improvements in the rainfall estimates, even though an underestimate of 24.6% by the radar QPE system in the total accumulated rainfall still exists as compared with gauge observations. The most significant improvement is realized by beam integration correction. The DSD-related corrections (i.e., convective–stratiform classification and local Z-R relationship) also lead to remarkable improvement and highlight the necessity of deriving the localized Z-R relationships for specific rainfall systems. The effectiveness of wind drift correction is then evaluated for a fast-moving case, whose results indicate both the total accumulation and the temporal characteristics of the rainfall estimates can be improved. In conclusion, considerable potential of X-band radar in high-resolution rainfall estimation can be realized by necessary error corrections.

scholarly journals Evaluation of satellite rainfall estimates over Ethiopian river basins

2010 ◽  
Vol 7 (5) ◽  
pp. 7669-7694 ◽  
Author(s):  
T. G. Romilly ◽  
M. Gebremichael
Keyword(s):  
High Resolution ◽  
Tropical Rainfall Measuring Mission ◽  
River Basins ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Annual Total ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Rainfall Estimates

Abstract. The objective of this study was to evaluate the accuracy of high resolution satellite-based rainfall estimates (SREs) across six river basins within Ethiopia during the major (Kiremt) and minor (Belg) rainy seasons for the years 2003 to 2007. The six regions, the Awash, Baro Akobo, Blue Nile, Genale Dawa, Rift Valley and Wabi Shebele River Basins surround the Ethiopian Highlands, which produces different topographical features, as well as spatial and temporal rainfall patterns. Precipitation estimates for the six regions were taken from three widely used high resolution SREs: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. All three SREs show the natural northwest-southeast precipitation gradient, but exhibit different spatial (mean annual total and number of rainy days) and temporal (monthly) totals. When compared to ground based rain gauges throughout the six regions, and for the years of interest, the performance of the three SREs were found to be season independent. The results varied for lower elevations, with CMORPH and TMPA 3B42RT performing better than PERSIANN in the southeast, while PERSIANN provided more accurate results in the northwest. At higher elevations, PERSIANN consistently underestimated while the performance of CMORPH and TMPA 3B42RT varied.

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