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- Author or Editor: Qing Wang x
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Abstract
Heavy rainfall and strong wind are the two main sources of disasters that are caused by tropical cyclones (TCs), and typhoons with different characteristics may induce different agricultural losses. Traditionally, the classification of typhoon intensity has not considered the amount of rainfall. Here, we propose a novel approach to calculate the typhoon type index (TTI). A positive TTI represents a “wind type” typhoon, where the overall damage in a certain area from TCs is dominated by strong wind. On the other hand, a negative TTI represents a “rain type” typhoon, where the overall damage in a certain area from TCs is dominated by heavy rainfall. From the TTI, the vulnerability of crop losses from different types of typhoons can be compared and explored. For example, Typhoon Kalmaegi (2008) was classified as a rain-type typhoon (TTI = −1.22). The most affected crops were oriental melons and leafy vegetables. On the contrary, Typhoon Soudelor (2015) was classified as a significant wind-type typhoon in most of Taiwan (TTI = 1.83), and the damaged crops were mainly bananas, bamboo shoots, pomelos, and other crops that are easily blown off by strong winds. Through the method that is proposed in this study, we can understand the characteristics of each typhoon that deviate from the general situation and explore the damages that are mainly caused by strong winds or heavy rainfall at different locations. This approach can provide very useful information that is important for the disaster analysis of different agricultural products.
Abstract
Heavy rainfall and strong wind are the two main sources of disasters that are caused by tropical cyclones (TCs), and typhoons with different characteristics may induce different agricultural losses. Traditionally, the classification of typhoon intensity has not considered the amount of rainfall. Here, we propose a novel approach to calculate the typhoon type index (TTI). A positive TTI represents a “wind type” typhoon, where the overall damage in a certain area from TCs is dominated by strong wind. On the other hand, a negative TTI represents a “rain type” typhoon, where the overall damage in a certain area from TCs is dominated by heavy rainfall. From the TTI, the vulnerability of crop losses from different types of typhoons can be compared and explored. For example, Typhoon Kalmaegi (2008) was classified as a rain-type typhoon (TTI = −1.22). The most affected crops were oriental melons and leafy vegetables. On the contrary, Typhoon Soudelor (2015) was classified as a significant wind-type typhoon in most of Taiwan (TTI = 1.83), and the damaged crops were mainly bananas, bamboo shoots, pomelos, and other crops that are easily blown off by strong winds. Through the method that is proposed in this study, we can understand the characteristics of each typhoon that deviate from the general situation and explore the damages that are mainly caused by strong winds or heavy rainfall at different locations. This approach can provide very useful information that is important for the disaster analysis of different agricultural products.
Abstract
In this study, we use observational and numerical model data from the Coupled Air Sea Processes and Electromagnetic Ducting Research (CASPER) field campaign to describe the mean refractive conditions offshore Duck, North Carolina. The U.S. Navy operational numerical weather prediction model known as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) performed well forecasting large-scale conditions during the experiment, with an observed warm bias in SST and cold and dry biases in temperature and humidity in the lowest 2000 m. In general, COAMPS underpredicted the number of ducts, and they were weaker and at lower height than those seen in observations. It was found that there is a noticeable diurnal evolution of the ducts, more over land than over the ocean. Ducts were found to be more frequent over land but overall were stronger and deeper over the ocean. Also, the evaporative duct height increases as one moves offshore. A case study was chosen to describe the electromagnetic properties under different synoptic conditions. In this case the continental atmospheric boundary layer dominates and interacts with the marine atmospheric boundary layer. As a result, the latter moves around 80 km offshore and then back inland after 2 h.
Abstract
In this study, we use observational and numerical model data from the Coupled Air Sea Processes and Electromagnetic Ducting Research (CASPER) field campaign to describe the mean refractive conditions offshore Duck, North Carolina. The U.S. Navy operational numerical weather prediction model known as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) performed well forecasting large-scale conditions during the experiment, with an observed warm bias in SST and cold and dry biases in temperature and humidity in the lowest 2000 m. In general, COAMPS underpredicted the number of ducts, and they were weaker and at lower height than those seen in observations. It was found that there is a noticeable diurnal evolution of the ducts, more over land than over the ocean. Ducts were found to be more frequent over land but overall were stronger and deeper over the ocean. Also, the evaporative duct height increases as one moves offshore. A case study was chosen to describe the electromagnetic properties under different synoptic conditions. In this case the continental atmospheric boundary layer dominates and interacts with the marine atmospheric boundary layer. As a result, the latter moves around 80 km offshore and then back inland after 2 h.
Deriving AMVs from Geostationary Satellite Images Using Optical Flow Algorithm Based on Polynomial ExpansionAbstract
An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.
Abstract
An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.
Abstract
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.
Abstract
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.
Abstract
Two aspects of Beijing cloudiness are studied: its relationship to other climate parameters during the period 1951–1990 and the reconstruction of proxy values between 1875 and 1950. For the recent period, cloudiness varies with no apparent trend and is highly correlated with the total number of rain days (r=0.77) and total sunshine duration (r=0.72). Good correlation is also found with maximum surface air temperature, surface relative humidity, and total precipitation. While the correlation between cloudiness and solar radiation was large prior to 1976, the coefficient for the period 1976–1990 is much smaller. This decrease can be attributed to a negative trend in solar radiation, which is consistent with an observed decrease in visibility. Variations in Beijing cloudiness are closely related to those found over most of northern China, while little similarity is found with locations south of 35°N.
The large correlation between annual cloudiness and the total number of rain days between 1951 and 1990 was used in conjunction with the observed rain day record for the period 1875–1950 to construct a proxy cloudiness record for Beijing for the period 1875–1950. Comparisons between proxy cloudiness and available observations of surface air temperature and relative humidity reveal that the relationships are consistent with those found when observed cloudiness is compared with observed temperature and humidity data. On the century time scale, there is no clear trend in percent cloudiness. However, on the decadal time scale, there is a negative trend in cloudiness during the period 1880–1930 followed by a period of relatively constant values between 1940 and 1975.
Abstract
Two aspects of Beijing cloudiness are studied: its relationship to other climate parameters during the period 1951–1990 and the reconstruction of proxy values between 1875 and 1950. For the recent period, cloudiness varies with no apparent trend and is highly correlated with the total number of rain days (r=0.77) and total sunshine duration (r=0.72). Good correlation is also found with maximum surface air temperature, surface relative humidity, and total precipitation. While the correlation between cloudiness and solar radiation was large prior to 1976, the coefficient for the period 1976–1990 is much smaller. This decrease can be attributed to a negative trend in solar radiation, which is consistent with an observed decrease in visibility. Variations in Beijing cloudiness are closely related to those found over most of northern China, while little similarity is found with locations south of 35°N.
The large correlation between annual cloudiness and the total number of rain days between 1951 and 1990 was used in conjunction with the observed rain day record for the period 1875–1950 to construct a proxy cloudiness record for Beijing for the period 1875–1950. Comparisons between proxy cloudiness and available observations of surface air temperature and relative humidity reveal that the relationships are consistent with those found when observed cloudiness is compared with observed temperature and humidity data. On the century time scale, there is no clear trend in percent cloudiness. However, on the decadal time scale, there is a negative trend in cloudiness during the period 1880–1930 followed by a period of relatively constant values between 1940 and 1975.
Abstract
There is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.
Abstract
There is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.
Abstract
In this paper, an integrated indicator-based system is established to map the suitability of spring soybean cultivation in northeast China. The indicator system incorporates both biophysical and socioeconomic factors, including the effects of temperature, precipitation, and sunshine on the individual development stages of the spring soybean life cycle. Spatial estimates of crop suitability derived using this indicator system are also compared with spring soybean planting areas to identify locations where there is scope for structural adjustment in soybean farming. Results of this study indicate that northeast China is moderately suited to spring soybean cultivation. Areas classified as suitable, moderately suitable, and unsuitable for soybean cultivation, respectively, occupy approximately 9.09 × 104, 11.45 × 104, and 7.99 × 104 km2, accounting for 11.5%, 10.11%, and 14.49% of the total area of northeast China. The Songnen and Sanjiang Plains are identified as the most and least suitable places, respectively, for spring soybean growth. A comparative analysis indicates that the suitable, moderately suitable, and unsuitable areas account for 24.78%, 46.30%, and 28.92%, respectively, of the total area presently under soybean cultivation. The analysis suggests that soybean cultivation in Heilongjiang Province is generally unfavorable, with equivalent percentages of 15.39%, 51.70%, and 32.91%. Results suggest that agricultural structural adjustment may be required to encourage farmers to grow spring soybeans. It is anticipated that this study will provide a basis for follow-up studies on crop cultivation suitability.
Abstract
In this paper, an integrated indicator-based system is established to map the suitability of spring soybean cultivation in northeast China. The indicator system incorporates both biophysical and socioeconomic factors, including the effects of temperature, precipitation, and sunshine on the individual development stages of the spring soybean life cycle. Spatial estimates of crop suitability derived using this indicator system are also compared with spring soybean planting areas to identify locations where there is scope for structural adjustment in soybean farming. Results of this study indicate that northeast China is moderately suited to spring soybean cultivation. Areas classified as suitable, moderately suitable, and unsuitable for soybean cultivation, respectively, occupy approximately 9.09 × 104, 11.45 × 104, and 7.99 × 104 km2, accounting for 11.5%, 10.11%, and 14.49% of the total area of northeast China. The Songnen and Sanjiang Plains are identified as the most and least suitable places, respectively, for spring soybean growth. A comparative analysis indicates that the suitable, moderately suitable, and unsuitable areas account for 24.78%, 46.30%, and 28.92%, respectively, of the total area presently under soybean cultivation. The analysis suggests that soybean cultivation in Heilongjiang Province is generally unfavorable, with equivalent percentages of 15.39%, 51.70%, and 32.91%. Results suggest that agricultural structural adjustment may be required to encourage farmers to grow spring soybeans. It is anticipated that this study will provide a basis for follow-up studies on crop cultivation suitability.
Abstract
On 4 October 2015, a miniature supercell embedded in an outer rainband of Typhoon Mujigae produced a major tornado in Guangdong province of China, leading to 4 deaths and up to 80 injuries. This study documents the structure and evolution of the tornadic miniature supercell using coastal Doppler radars, a sounding, videos, and a damage survey. This tornado is rated at least EF3 on the enhanced Fujita scale. It is by far the strongest typhoon rainband tornado yet documented in China, and possessed double funnels near its peak intensity.
Radar analysis indicates that this tornadic miniature supercell exhibited characteristics similar to those found in United States landfalling hurricanes, including a hook echo, low-level inf low notches, an echo top below 10 km, a small and shallow mesocyclone, and a long lifespan (3 h). The environmental conditions—which consisted of moderate convective available potential energy (CAPE), a low lifting condensation level, a small surface dewpoint depression, a large veering low-level vertical wind shear, and a large cell-relative helicity—are favorable for producing miniature supercells. The mesocyclone, with its maximum intensity at 2 km above ground level (AGL), formed an hour before tornadogenesis. A tornado vortex signature (TVS) was identified between 1 and 3 km AGL, when the parent mesocyclone reached its peak radar-indicated intensity of 30 m s−1. The TVS was located between the updraft and forward-flank downdraft, near the center of the mesocyclone. Dual-Doppler wind analysis reveals that tilting of the low-level vorticity into the vertical direction and subsequent stretching by a strong updraft were the main contributors to the mesocyclone intensification.
Abstract
On 4 October 2015, a miniature supercell embedded in an outer rainband of Typhoon Mujigae produced a major tornado in Guangdong province of China, leading to 4 deaths and up to 80 injuries. This study documents the structure and evolution of the tornadic miniature supercell using coastal Doppler radars, a sounding, videos, and a damage survey. This tornado is rated at least EF3 on the enhanced Fujita scale. It is by far the strongest typhoon rainband tornado yet documented in China, and possessed double funnels near its peak intensity.
Radar analysis indicates that this tornadic miniature supercell exhibited characteristics similar to those found in United States landfalling hurricanes, including a hook echo, low-level inf low notches, an echo top below 10 km, a small and shallow mesocyclone, and a long lifespan (3 h). The environmental conditions—which consisted of moderate convective available potential energy (CAPE), a low lifting condensation level, a small surface dewpoint depression, a large veering low-level vertical wind shear, and a large cell-relative helicity—are favorable for producing miniature supercells. The mesocyclone, with its maximum intensity at 2 km above ground level (AGL), formed an hour before tornadogenesis. A tornado vortex signature (TVS) was identified between 1 and 3 km AGL, when the parent mesocyclone reached its peak radar-indicated intensity of 30 m s−1. The TVS was located between the updraft and forward-flank downdraft, near the center of the mesocyclone. Dual-Doppler wind analysis reveals that tilting of the low-level vorticity into the vertical direction and subsequent stretching by a strong updraft were the main contributors to the mesocyclone intensification.
A tornado outbreak occurred in central Oklahoma on 10 May 2010, including two tornadoes with enhanced Fujita scale ratings of 4 (EF-4). Tragically, three deaths were reported along with significant property damage. Several strong and violent tornadoes occurred near Norman, Oklahoma, which is a major hub for severe storms research and is arguably one of the best observed regions of the country with multiple Doppler radars operated by both the federal government and the University of Oklahoma (OU). One of the most recent additions to the radars in Norman is the high-resolution OU Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME). As the name implies, the radar is used as a platform for research and education in both science and engineering studies using polarimetric radar. To facilitate usage of the system by students and faculty, OU-PRIME was constructed adjacent to the National Weather Center building on the OU research campus. On 10 May 2010, several tornadoes formed near the campus while OU researchers were operating OU-PRIME in a sector scanning mode, providing polarimetric radar data with unprecedented resolution and quality. In this article, the environmental conditions leading to the 10 May 2010 outbreak will be described, an overview of OU-PRIME will be provided, and several examples of the data and possible applications will be summarized. These examples will highlight supercell polarimetric signatures during and after tornadogenesis, and they will describe how the polarimetric signatures are related to observations of reflectivity and velocity.
A tornado outbreak occurred in central Oklahoma on 10 May 2010, including two tornadoes with enhanced Fujita scale ratings of 4 (EF-4). Tragically, three deaths were reported along with significant property damage. Several strong and violent tornadoes occurred near Norman, Oklahoma, which is a major hub for severe storms research and is arguably one of the best observed regions of the country with multiple Doppler radars operated by both the federal government and the University of Oklahoma (OU). One of the most recent additions to the radars in Norman is the high-resolution OU Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME). As the name implies, the radar is used as a platform for research and education in both science and engineering studies using polarimetric radar. To facilitate usage of the system by students and faculty, OU-PRIME was constructed adjacent to the National Weather Center building on the OU research campus. On 10 May 2010, several tornadoes formed near the campus while OU researchers were operating OU-PRIME in a sector scanning mode, providing polarimetric radar data with unprecedented resolution and quality. In this article, the environmental conditions leading to the 10 May 2010 outbreak will be described, an overview of OU-PRIME will be provided, and several examples of the data and possible applications will be summarized. These examples will highlight supercell polarimetric signatures during and after tornadogenesis, and they will describe how the polarimetric signatures are related to observations of reflectivity and velocity.
