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

Li, Jianfeng; Feng, Zhe; Qian, Yun; Leung, L. Ruby

doi:10.5194/essd-2020-151

Cited by 7 publications

(25 citation statements)

References 53 publications

(76 reference statements)

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“…As shown in Figures S5 and S6, generally, both the 36-km and nested 4-km WRF simulations predict much less precipitation (in precipitation amount and duration) compared to Stage-IV in July 2011 around the DISCOVER-AQ campaign region, especially for the nested 4-km WRF simulation. We find that large-scale precipitation amounts are much less compared to convective precipitation in most regions in the 36-km WRF simulation (Figure S7) during the simulation period, which is contradictory to Li et al (2020) showing non-convective precipitation accounting for 25% -40% of the total precipitation. At 4-km resolution, convective and non-convective precipitations are not separated because convection is explicitly resolved.…”

Section: Datasets and Model Descriptioncontrasting

confidence: 78%

Comment on acp-2020-1193

2021

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Nitrogen oxides (NOx = NO + NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations of NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements, surface EPA Air Quality System (AQS) observations, as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; and GOME-2A: Global Ozone Monitoring Experiment -2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument, in July 2011 during the DISCOVER-AQ campaign over the Baltimore-Washington region. The model simulations at 36-and 4-km resolutions are in reasonably good agreement with the temporospatial NO2 observations in the daytime. However, nighttime mixing in the model needs to be enhanced to reproduce the observed NO2 diurnal cycle in the model. Another discrepancy is that Pandora measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. Relative to the 36-km model simulations, the 4km model results show larger biases compared to the observations due largely to the larger spatial variations of NO2 in the model when the spatial resolution is increased from 36 to 4 km, although the biases are often comparable to the ranges of the observations. The high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than 4-km model simulations, reflecting likely the spatial distribution bias of NOx emissions in the National Emissions Inventory (NEI) 2011 at high resolution.

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Section: Datasets and Model Descriptioncontrasting

confidence: 78%

Comment on acp-2020-1193

2021

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“…The MCS/IDC data set is a high‐resolution (4 km, hourly) observational data product containing the classification, tracking, and characteristics of MCS and IDC events in the U.S. east of the Rocky Mountains from 2004 to 2017 (Li et al., 2021). The data product is developed by using the Storm Labeling in Three Dimensions (SL3D) algorithm (Starzec et al., 2017) and an updated FLEXTRKR (Flexible Object Tracker) algorithm (Feng et al., 2019; Li et al., 2021) based on the National Centers for Environmental Prediction (NCEP)/the Climate Prediction Center (CPP) L3 4 km Global Merged IR V1 brightness temperature ( T b ) data set (Janowiak et al., 2017), the three‐dimensional (3‐D) Gridded NEXRAD Radar (Gridrad) data set (Homeyer & Bowman, 2017), and the NCEP Stage IV precipitation data set (Lin & Mitchell, 2005). In the data set, an MCS is defined as a cold cloud system (CCS) ( T b < 241 K) with CCS areas surpassing 60,000 km 2 for more than six continuous hours and containing at least six consecutive hours of Precipitation Feature (PF) with major axis length >100 km and embedded intense convective cell area ≥16 km 2 .…”

Section: Data Sets and Methodsmentioning

confidence: 99%

“…allowing us to investigate the contributions of different precipitation types to extreme daily precipitation. To analyze the sub‐daily extreme precipitation characteristics due to the <1‐day precipitation durations of most MCS/IDC events (Li et al., 2021), we also calculate the summertime top 1%/5% extreme hourly precipitation for each grid cell over the MAR by using a similar definition as that for extreme daily precipitation. Here, wet days/hours are days/hours with accumulated daily/hourly precipitation >0.01 mm (Layberry et al., 2006; X. Li et al., 2020; Piani et al., 2010).…”

Section: Data Sets and Methodsmentioning

confidence: 99%

“…The MCS/IDC data set is available at https://data.pnnl.gov/dataset/13218 (last access: June 18, 2020) (J. Li et al., 2020; Li et al., 2021). The authors download the IBTrACS version 4.0 data set from https://www.ncdc.noaa.gov/ibtracs/index.php%3Fname%3Dib%2Dv4%2Daccess (last access: May 7, 2020) (Knapp et al., 2018).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…The MCS/IDC data set is available at https://data.pnnl.gov/dataset/13218 (last access: June 18, 2020) (J. Li et al, 2021). The authors download the IBTrACS version 4.0 data set from https://www.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Summer Mean and Extreme Precipitation Over the Mid‐Atlantic Region: Climatological Characteristics and Contributions From Different Precipitation Types

Qian

Leung

et al. 2021

JGR Atmospheres

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Based on a long‐term observational data set from the tracking of mesoscale convective systems (MCSs), isolated deep convection (IDC), and tropical cyclones (TCs), we examine the climatological characteristics of summer (June–August) mean and extreme precipitation during 2004–2017 and their respective contributions from MCS, IDC, TC, and non‐convective (NC) systems and the local versus remote influence of MCS and IDC over the Mid‐Atlantic region (MAR). On average, MCS, IDC, TC, and NC contribute 22%, 29%, 4%, and 45% to the total summer mean precipitation in the region. While MCS and TC precipitation primarily occurs in the coastal areas east of the Appalachian Mountains, IDC precipitation is more concentrated in the southern MAR and near the mountain windward slopes. Each summer, ∼41 MCSs with an average lifetime of 19.6 h influence the MAR, with 80% initiated outside and traveling on average 10 h before their centroids enter the region. Around 13 MCSs initiated in the Great Plains and Midwest propagate across the Appalachian Mountains and contribute 20%–40% to MCS precipitation in the central Mid‐Atlantic coastal areas each summer. In contrast, ∼2000 IDC events with an average lifetime of 2.0 h influence the MAR each summer, and 77% are initiated locally. MCS, IDC, TC, and NC contribute 31% (30%), 26% (31%), 17% (7%), and 26% (32%) to the top 1% (5%) extreme daily precipitation, respectively. Considering extreme hourly precipitation, however, the IDC contributions increase to 41% (top 1%) and 38% (top 5%) due to the frequent occurrence and large intensity of IDC precipitation.

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Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

et al. 2022

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The total surface area of the Great Lakes is over 94,250 miles 2 . Consisting of five large lakes, they contain over one fifth of the world's total surface fresh water, with depths of up to 400 m (EPA, 2020). The Great Lakes substantially influence the regional weather and climate via their effects on the atmospheric energy and water budget; open water typically has greater thermal conductance, lower albedo, and lower roughness compared to soil or vegetated surfaces (Notaro et al.,2013;Scott & Huff, 1996). The large thermal inertia of the lakes leads to a reduction in annual and diurnal temperature ranges across the Great Lakes Basin (Harris & Kotamarthi, 2005;Notaro et al., 2013). Mean minimum temperatures in the region are warmer during all seasons and over all five lakes, while maximum temperatures are cooler during spring and summer (Scott & Huff, 1996). Lake thermal impacts and associated evaporation have a strong influence on regional precipitation patterns. While many studies have investigated the impact of lake surface temperature (LST) on the cold-season precipitationwhich is primarily a direct product of warmth and moisture from the Great Lakes (e.g., lake-effect snow; Notaro et al., 2021; Shi & Xue, 2019)-the impact of LST on summer precipitation has only rarely been studied. During early summer, the lake surface is still relatively cool compared to the atmosphere, resulting in condensation on the lake surface and a stable boundary layer that deters over-lake convective processes (Miner & Fritsch, 1997;Workoff et al., 2012). Due to the temperature differences over lake and inland, lake breeze can cause air to rise inland and increases the likelihood of over-land precipitation (Schulkowski, 2020). During late summer, while the land cools and the lakes remain warm, the temperature differential between land and lake surface coupled with baroclinic waves (e.g., cold front and trough) and/or the Great Plains low-level jets can generate enhanced precipitation (Feng et al., 2016;Miner & Fritsch, 1997). Therefore, changes in summer LST could potentially affect its feedback to atmospheric stability and change the water and energy budget over the entire Great Lakes Region (GLR). The summer is an ideal time to evaluate the influence of LST on the lake surface-atmosphere

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

Cited by 7 publications

References 53 publications

Comment on acp-2020-1193

Comment on acp-2020-1193

Summer Mean and Extreme Precipitation Over the Mid‐Atlantic Region: Climatological Characteristics and Contributions From Different Precipitation Types

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

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