scholarly journals Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature

2015 ◽  
Vol 19 (4) ◽  
pp. 1753-1766 ◽  
Author(s):  
P. Molnar ◽  
S. Fatichi ◽  
L. Gaál ◽  
J. Szolgay ◽  
P. Burlando
Keyword(s):  
Empirical Study ◽  
Air Temperature ◽  
Extreme Precipitation ◽  
Interval Data ◽  
Storm Events ◽  
Data Set ◽  
Convective Storms ◽  
Regional Effects ◽  
Moisture Supply ◽  
Alpine Environments

Abstract. Extreme precipitation is thought to increase with warming at rates similar to or greater than the water vapour holding capacity of the air at ~ 7% °C−1, the so-called Clausius–Clapeyron (CC) rate. We present an empirical study of the variability in the rates of increase in precipitation intensity with air temperature using 30 years of 10 min and 1 h data from 59 stations in Switzerland. The analysis is conducted on storm events rather than fixed interval data, and divided into storm type subsets based on the presence of lightning which is expected to indicate convection. The average rates of increase in extremes (95th percentile) of mean event intensity computed from 10 min data are 6.5% °C−1 (no-lightning events), 8.9% °C−1 (lightning events) and 10.7% °C−1 (all events combined). For peak 10 min intensities during an event the rates are 6.9% °C−1 (no-lightning events), 9.3% °C−1 (lightning events) and 13.0% °C−1 (all events combined). Mixing of the two storm types exaggerates the relations to air temperature. Doubled CC rates reported by other studies are an exception in our data set, even in convective rain. The large spatial variability in scaling rates across Switzerland suggests that both local (orographic) and regional effects limit moisture supply and availability in Alpine environments, especially in mountain valleys. The estimated number of convective events has increased across Switzerland in the last 30 years, with 30% of the stations showing statistically significant changes. The changes in intense convective storms with higher temperatures may be relevant for hydrological risk connected with those events in the future.

scholarly journals Little evidence for super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature

2014 ◽  
Vol 11 (7) ◽  
pp. 8923-8948 ◽  
Author(s):  
P. Molnar ◽  
S. Fatichi ◽  
C. Berger ◽  
I. Ismail ◽  
L. Gaál ◽  
...  
Keyword(s):  
Air Temperature ◽  
Fixed Time ◽  
Significant Shift ◽  
Storm Events ◽  
Alpine Environment ◽  
Lightning Strikes ◽  
Convective Storms ◽  
Regional Effects ◽  
Hourly Data ◽  
Alpine Environments

Abstract. Extreme precipitation is thought to increase proportionally to the rise in the water vapor holding capacity of the air at roughly 7% °C−1, the so called Clausius–Clapeyron (CC) rate. We present an empirical study of the variability in the rates of increase in precipitation intensity with air temperature using 30 yr of hourly data from 50 stations in an Alpine environment. The analysis is conducted on storm events rather than fixed time resolutions, and divided into event subsets based on concurrent lightning strikes indicating the presence of convection. The average rates of increase in mean event intensity (7.4% °C−1) and peak hourly intensity (5.1% °C−1) for 90th percentiles are close to the CC rate expected under fully saturated conditions. Super-CC rates reported by other studies are an exception in our dataset. Events accompanied by lightning (convective events) exhibit significantly higher rates of increase than stratiform rain. Mixing of the two storm types exaggerates the relations to air temperature. The large spatial variability in scaling rates across Switzerland suggests that both local (orographic) and regional effects limit moisture availability and supply in Alpine environments especially in mountain valleys. A trend analysis shows that our estimate of the number of convective events across Switzerland has steadily increased in the last 30 yr. This significant shift towards more convective storms in a warming climate may as a consequence lead to stronger storm intensities and therefore higher risk connected with those events.

Changes in mechanisms and intensity of Western U.S. floods, 1960-2013

2021 ◽  
Author(s):  
Jessica Fayne ◽  
Huilin Huang ◽  
Mike Fischella ◽  
Yufei Liu ◽  
Zhaoxin Ban ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Critical Factor ◽  
Flood Frequency ◽  
Atmospheric Rivers ◽  
Convective Storms ◽  
Noticeable Increase ◽  
Complex Interactions ◽  
Different Types ◽  
The Impact

<p>Extreme precipitation, a critical factor in flooding, has selectively increased with warmer temperatures in the Western U.S. Despite this, the streamflow measurements have captured no noticeable increase in large-scale flood frequency or intensity. As flood studies have mostly focused on specific flood events in particular areas, analyses of large-scale floods and their changes have been scarce. For floods during 1960-2013, we identify six flood generating mechanisms (FGMs) that are prominent across the Western U.S., including atmospheric rivers and non-atmospheric rivers, monsoons, convective storms, radiation-driven snowmelt, and rain-on-snow, in order to identify to what extent different types of floods are changing based on the dominant FGM. The inconsistency between extreme precipitation and lack of flood increase suggests that the impact of climate change on flood risk has been modulated by hydro-meteorological and physiographic processes such as sharp increases in temperature that drive increased evapotranspiration and decreased soil moisture. Our results emphasize the importance of FGMs in understanding the complex interactions of flooding and climatic changes and explain the broad spatiotemporal changes that have occurred across the vast Western U.S. for the past 50 years.</p>

scholarly journals High-resolution analysis of 1 day extreme precipitation in Sicily

2015 ◽  
Vol 15 (10) ◽  
pp. 2347-2358 ◽  
Author(s):  
M. Maugeri ◽  
M. Brunetti ◽  
M. Garzoglio ◽  
C. Simolo
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Human Life ◽  
Extreme Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Regional Frequency Analysis ◽  
Economic Losses ◽  
Data Set ◽  
High Quantiles

Abstract. Sicily, a major Mediterranean island, has experienced several exceptional precipitation episodes and floods during the last century, with serious damage to human life and the environment. Long-term, rational planning of urban development is indispensable to protect the population and to avoid huge economic losses in the future. This requires a thorough knowledge of the distributional features of extreme precipitation over the complex territory of Sicily. In this study, we perform a detailed investigation of observed 1 day precipitation extremes and their frequency distribution, based on a dense data set of high-quality, homogenized station records in 1921–2005. We estimate very high quantiles (return levels) corresponding to 10-, 50- and 100-year return periods, as predicted by a generalized extreme value distribution. Return level estimates are produced on a regular high-resolution grid (30 arcsec) using a variant of regional frequency analysis combined with regression techniques. Results clearly reflect the complexity of this region, and show the high vulnerability of its eastern and northeastern parts as those prone to the most intense and potentially damaging events.

scholarly journals Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

2021 ◽  
pp. 33-41
Author(s):  
Joyce Imara Nchom ◽  
A. S. Abubakar ◽  
F. O. Arimoro ◽  
B. Y. Mohammed
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Linear Trend ◽  
Pearson Correlation ◽  
Maximum Temperature ◽  
Data Set ◽  
International Research Institute ◽  
Wind Speed Maximum ◽  
Weather Parameters ◽  
The Relationship

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations

2021 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Precipitation Events

<p>Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 – 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). <span>For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.</span></p>

Quantifying heating biases in marine air temperature observations, ~1790 - present

2021 ◽  
Author(s):  
Thomas Cropper ◽  
Elizabeth Kent ◽  
David Berry ◽  
Richard Cornes ◽  
Beatriz Recinos-Rivas
Keyword(s):  
Air Temperature ◽  
Diurnal Cycle ◽  
Surface Air Temperature ◽  
Night Time ◽  
Data Set ◽  
Near Surface ◽  
Marine Surface ◽  
Heating Model ◽  
Marine Air

<p>Accurate, long-term time series of near-surface air temperature (AT) are the fundamental datasets on which the magnitude of anthropogenic climate change is scientifically and societally addressed. Across the ocean, these (near-surface) climate records use Sea Surface Temperature (SST) instead of Marine Air Temperature (MAT) and blend the SST and AT over land to create datasets. MAT has often been overlooked as a data choice as daytime MAT observations from ships are known to contain warm biases due to the storage of accumulated solar energy. Two recent MAT datasets, CLASSnmat (1881 – 2019) and UAHNMAT (1900 – 2018), both use night-time MAT observations only. Daytime MAT observations in the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) account for over half of the MAT observations in ICOADS, and this proportion increases further back in time (i.e. pre-1850s). If long-term MAT records over the ocean are to be extended, the use of daytime MAT is vital.</p><p> </p><p>To adjust for the daytime MAT heating bias, and apply it to ICOADS, we present the application of a physics-based model, which accounts for the accumulated energy storage throughout the day. As the ‘true’ diurnal cycle of MAT over the ocean has not been, to-date, adequately quantified, our approach also removes the diurnal cycle from ICOADS observations and generates a night-time equivalent MAT for all observations. We fit this model to MAT observations from groups of ships in ICOADS that share similar heating biases and metadata characteristics. This enables us to use the empirically derived coefficients (representing the physical energy transfer terms of the heating model) obtained from the fit for use in removal of the heating bias and diurnal cycle from ship-based MAT observations throughout ICOADS which share similar characteristics (i.e. we can remove the diurnal cycle from a ship which only reports once daily at noon). This adjustment will create an MAT record of night-time-equivalent temperatures that will enable an extension of the marine surface AT record back into the 18<sup>th</sup> century.</p>

scholarly journals Personal Atmosphere: Estimation of Air Conditioner Parameters for Personalizing Thermal Comfort

2020 ◽  
Vol 10 (22) ◽  
pp. 8067
Author(s):  
Tomohiro Mashita ◽  
Tetsuya Kanayama ◽  
Photchara Ratsamee
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Air Temperature ◽  
Fine Tuning ◽  
Air Conditioner ◽  
Data Set ◽  
Air Conditioning System ◽  
Air Conditioners ◽  
Uniform Distributions ◽  
Machine Learning Model

Air conditioners enable a comfortable environment for people in a variety of scenarios. However, in the case of a room with multiple people, the specific comfort for a particular person is highly dependent on their clothes, metabolism, preference, and so on, and the ideal conditions for each person in a room can conflict with each other. An ideal way to resolve these kinds of conflicts is an intelligent air conditioning system that can independently control air temperature and flow at different areas in a room and then produce thermal comfort for multiple users, which we define as the personal preference of air flow and temperature. In this paper, we propose Personal Atmosphere, a machine learning based method to obtain parameters of air conditioners which generate non-uniform distributions of air temperature and flow in a room. In this method, two dimensional air-temperature and -flow distributions in a room are used as input to a machine learning model. These inputs can be considered a summary of each user’s preference. Then the model outputs a parameter set for air conditioners in a given room. We utilized ResNet-50 as the model and generated a data set of air temperature and flow distributions using computational fluid dynamics (CFD) software. We then conducted evaluations with two rooms that have two and four air conditioners under the ceiling. We then confirmed that the estimated parameters of the air conditioners can generate air temperature and flow distributions close to those required in simulation. We also evaluated the performance of a ResNet-50 with fine tuning. This result shows that its learning time is significantly decreased, but performance is also decreased.

Predicting Activity Levels in Virtual Communities

2011 ◽  
pp. 387-389
Author(s):  
Ori B. Kushnir
Keyword(s):  
Virtual Communities ◽  
Interval Data ◽  
Activity Level ◽  
Data Series ◽  
Time Interval ◽  
Activity Levels ◽  
Data Set ◽  
Back Testing ◽  
One Year ◽  
Testing Standards

Data was collected from two communities: a smaller community with approximately 200 participants, but where the number of participants is precisely known; and a very large community, with thousands of participants, where the number of participants can only be estimated from the number of different nicknames used within a given time interval. Data from certain days when there were documented technical issues that may have affected activity has been removed from the sample. In both cases, we have taken one geographically centric data series and one global series, covering users in multiple areas and time zones. We use the number of messages sent in three-hour intervals as a proxy for the activity level in a community, as accurate figures regarding the number of messages viewed by unique persons are difficult to establish. This results in a data set of approximately 9,500 samples from each community, collected over a period of just less than four years. When fitting the models, we used accepted back-testing standards, relying on a fixed interval (one year) when fitting parameters and forecasting activity for any given point in time. One exception to this is seasonality adjustment, where we used the entire data set—this should not have a significant effect, as we made the same seasonal adjustment to the input for all models. Empirical results provided throughout the article are based on data from the larger community.

scholarly journals Characteristics of Heavy Storms and the Scaling Relation with Air Temperature by Event Process-Based Analysis in South China

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 185 ◽  
Author(s):  
Cuilin Pan ◽  
Xianwei Wang ◽  
Lin Liu ◽  
Dashan Wang ◽  
Huabing Huang
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
South China ◽  
Meteorological Factors ◽  
Rainfall Data ◽  
Precipitation Extremes ◽  
Scaling Relation ◽  
Convective Storms ◽  
Warm Front ◽  
Storm Characteristics

The negative scaling rate between precipitation extremes and the air temperature in tropic and subtropic regions is still a puzzling issue. This study investigates the scaling rate from two aspects, storm characteristics (types) and event process-based temperature variations. Heavy storms in South China are developed by different weather systems with unique meteorological characteristics each season, such as the warm-front storms (January), cold-front storms (April to mid-May), monsoon storms (late May to June), convective storms, and typhoon storms (July to September). This study analyzes the storm characteristics using the hourly rainfall data from 1990 to 2017; compares the storm hyetographs derived from the one-minute rainfall data during 2008–2017; and investigates the interactions between heavy storms and meteorological factors including air temperature, relative humidity, surface pressure, and wind speed at 42 weather stations in Guangzhou during 2015–2017. Most storms, except for typhoon and warm-front storms, had a short duration (3 h) and intense rates (~13 mm/h) in Guangzhou, South China. Convective storms were dominant (50%) in occurrence and had the strongest intensity (15.8 mm/h). Storms in urban areas had stronger interactions with meteorological factors and showed different hyetographs from suburban areas. Meteorological factors had larger variations with the storms that occurred in the day time than at night. The air temperature could rise 6 °C and drop 4 °C prior to and post-summer storms against the diurnal mean state. The 24-hour mean air temperature prior to the storms produced more reliable scaling rates than the naturally daily mean air temperature. The precipitation extremes showed a peak-like scaling relation with the 24-hour mean air temperature and had a break temperature of 28 °C. Below 28 °C, the relative humidity was 80%–100%, and it showed a positive scaling rate. Above 28 °C, the negative scaling relation was likely caused by a lack of moisture in the atmosphere, where the relative humidity decreased with the air temperature increase.

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