Surface Meteorological Observations Research Articles

Observational Study on Boundary Layer Moist Static Energy Budget over the Tropical Western Pacific and Its Variability Associated with Boreal Summer Intraseasonal Oscillation

Abstract Moist static energy (MSE) in the atmospheric boundary layer (BL) is one of the essential parameters determining convective activity over tropical oceanic areas. It is thus important to quantitatively understand BL MSE budget processes and their variability. Among these processes, only few studies have evaluated contributions of entrainment across the BL top and convective downdraft. This study aims to estimate these contributions by analyzing upper-air and surface meteorological observations obtained using research vessel Mirai over the tropical western Pacific in June 2008. Daily mean downward mass fluxes due to the two processes are calculated using BL dry static energy and moisture budget equations under the BL quasi-equilibrium approximation. Estimated mass fluxes are consistent with convective activity observed by a shipborne weather radar and a ceilometer. This study further examines how the mass fluxes and budget processes are modulated when a convectively active phase of boreal summer intraseasonal oscillation arrives at the observation area in the second half of the month. It is found that, while the contribution of the entrainment does not change significantly, the convective downdraft mass flux and the resultant BL MSE export increase 5 times and 3 times, respectively, in the convectively active period compared with those in the preactive period. Furthermore, ∼1/4 of the increase in the convective downdraft mass flux is attributable to the increase in MSE of convective downdraft air associated with midtropospheric moistening.

Characteristics Analysis of Precipitation Observations and Climate Change Along the High-speed Railway in Jiangsu

<p>High-speed railway (HSR) is severely affected by precipitation disasters. There are many studies on precipitation and precipitation disasters, most of which are precipitation and analysis of disasters caused by precipitation, and few explain climate changes caused by precipitation. This paper uses surface meteorological observation data within 90 km along the Jiangsu section of the Beijing-Shanghai HSR to study the characteristics of precipitation along the line in 2016 ~ 2018 and the characteristics of climate change before the occurrence of precipitation events. This paper presents that when air pressure along the HSR with a relatively low level and a higher probability of precipitation. The low temperatures in April, June and July are more likely to produce precipitation. Correlations analysis shows that the three types of precipitation events are different. The time scale of the correlation between temperature and pressure changes before the occurrence of mild to moderate precipitation events is farther, and the temperature and short-term pressure changes 6 hours before the occurrence of heavy precipitation events. In contrast, the air pressure and temperature within 2 hours before the occurrence of extreme precipitation change rapidly and have a strong correlation, which indicates the occurrence of extreme events.</p>

ИНТЕГРАЛЬНАЯ ОЦЕНКА БИОКЛИМАТИЧЕСКИХ ХАРАКТЕРИСТИК УЛЬЯНОВСКОЙ ОБЛАСТИ В ТЕРРИТОРИАЛЬНОМ РАЗРЕЗЕ

The article discusses the main approaches to increasing the volume of grain production and maximizing the use of bioclimatic potential in changing climatic conditions, and studying comprehensive assessments of the factors of potential and actually possible production by crops. The initial information for the research was the data of surface meteorological observations from 1990 to 2019 at six meteorological stations located on the territory of the Ulyanovsk region. In assessing long-term changes in agrometeorological parameters, Fourier expansion was used, and the parameters of the best sinusoidal approximation of the maximum and minimum points were determined. It was revealed that over the past three decades, there has been a steady increase in the average annual temperature (5.2 ° C), especially in the cold season. Moisture supply is more chaotic, the change is heterogeneous, varying from 280 mm (2012) to 664 mm (2017) and the average value for 1990-2019 is 498 mm. Barley (8.3 t/ha) has the greatest potential among grain crops in terms of photosynthetically active radiation (PAR), rye has the smallest (6.4 t/ha). In terms of the amount of thermal resources and moisture, rye reaches the maximum crop production, winter wheat is sensitive to moisture, and barley to temperature conditions. Moisture resources are the limiting factor in agricultural production, and therefore the level of use of bioclimatic potential varies from 19 to 29% in PAR, from 25 to 44% in moisture resources, and from 32.8 to 66% in thermal regime. Therefore, among the priority problems associated with adaptation work is analytical and practical activities in the field of assessment and analysis of the transformation of agriculture, ensuring its sustainability for adaptation to modern changes in climatic conditions. To adapt to modern changes in climatic conditions, it is necessary to improve the structure of sown areas, the use of effective fertilizers, technical equipment, the use of elite seeds, modern technologies. The scientific novelty of the research lies in the objective function of the bioclimatic potential of development against the background of global warming, the consideration of the system of potentials for the agricultural development of the region. The work is of practical importance, since the assessments being developed are a necessary tool in ensuring the target orientation of a unified regional policy in relation to the formation and use of development potential, including contributing to the formation of an idea of the real and potential opportunities of the region.

Case Study on a Snowfall Event in Beijing from March 17 to 18, 2022

On March 17, 2022, a city-wide snowfall occurred in Beijing, China, with significant snowfall in Northwest China and other places. By using the surface meteorological observation data and NCEP/NCAR reanalysis data, we carried out a case study of a snowfall event in Beijing from March 17 to 18, 2022. Results show that this process had the characteristics of a wide range of influence and a large range of cooling. The temperature in the north of the middle and lower reaches of the Yangtze River changed from an obvious high to low in the previous period, and the rain and snow in northern China were obvious. The snowfall in Beijing is the result of the combined influence of warm and humid air and cold air. The warm and humid air flow from the Indo-China Peninsula all the way to the north brought abundant water vapor and unstable energy. The southward cold air and warm and humid air met and collided in the central and eastern part of China, and brought about precipitation under the combined action of atmospheric instability energy and other factors. The temperature in Beijing dropped significantly, but it still has not reached the standard of “cold spring” in the meteorological sense.

Observations of a Downburst and a Tornado by a High Density Ground Surface Meteorological Observation Network

Observations of a Downburst and a Tornado by a High Density Ground Surface Meteorological Observation Network

Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland

Abstract The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=−0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.

The Temporal-Spatial Variations and Potential Causes of Dust Events in Xinjiang Basin During 1960–2015

Dust events not only cause local ecosystem degradation and desertification, but also have profound impacts on regional and global climate system, as well as air quality and human health. Dust events in Xinjiang Basin, as the important dust source of Eastern Asia, have undergone a significant change under the global warming background and may be in a new active period after 2000, which is worthy of study. This study provides the temporal and spatial variations of dust events in the Xinjiang Basin based on surface meteorological station observation data during 1960–2015. The results show that Southern Xinjiang is the main dust occurrence region where dust events are significantly more than that in the Northern Xinjiang, and each year more than 73% of dust events occurred in spring and summer. The dust index (DI), which is defined to represent the large-scale variation of dust event, shows a significant downward trend during the past 56 years with a linear decreasing rate −8.2 years−1in Southern Xinjiang. The DI is positively correlated to surface wind speed with a mean correlation coefficient of 0.79. The declining trend of surface wind speed could explain dust events variation during 1960–2000. But in the new active period after 2000, the increase of DI is not consistent with the rising wind speed with the correlation coefficient decreasing to 0.34. It is found that, compared with 1960–1999, the average annual precipitation and frequency increased by 17.4 and 13% during 2000–2015, respectively, and the NDVI also increased at the same time, which indicates that the surface condition changes induced by the increase of precipitation might suppress the occurrence of dust. Moreover, the analysis of high-altitude wind field shows that the variation of the East Asian general circulation’s intensity, dominating the upper-level wind fields in the Xinjiang basin, will change the surface wind speed and precipitation, and further affect the occurrence of dust events.

AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

Previous studies on the impact of urbanization on the diurnal temperature range (DTR) have mainly concentrated on the intra-seasonal and interannual–decadal scales, while relatively fewer studies have considered synoptic scales. In particular, the modulation of DTR by different synoptic weather patterns (SWPs) is not yet fully understood. Taking the urban agglomeration of the Yangtze River Delta region (YRDUA) in eastern China as an example, and by using random forest machine learning and objective weather classification methods, this paper analyzes the characteristics of DTR and its urban–rural differences (DTRU–R) in summer from 2013 to 2016, based on surface meteorological observations, satellite remote sensing, and reanalysis data. Ultimately, the influences of urbanization-related factors and different large-scale SWPs on DTR and DTRU–R are explored. Results show that YRDUA is controlled by four SWPs in the 850-hPa geopotential height field in summer, and the DTRs in three sub-regions are significantly different under the four SWPs, indicating that they play a role in regulating the DTR in YRDUA. In terms of the average DTR for each SWP, the southern sub-region of the YRDUA is the highest, followed by the northern sub-region, and the middle sub-region is the lowest, which is most significantly affected by high-level urbanization and high anthropogenic heat emission. The DTRU–R is negative and differs under the four different SWPs with variation in sunshine and rainfall. The difference in anthropogenic heat flux between urban and rural areas is one of the potentially important urbanization-related drivers for DTRU–R. Our findings help towards furthering our understanding of the response of DTR in urban agglomerations to different SWPs via the modulation of local meteorological conditions.

Evaluation of a land-atmosphere coupling metric computed from a ground-based infrared interferometer

AbstractLand-atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land-atmosphere coupling metrics. Ground based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high temporal resolution vertical profiles of temperature and moisture. When co-located with soil moisture, surface flux, and surface meteorological observations, such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, it is possible to observe both the terrestrial and atmospheric legs of land-atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data to that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde and AERI based observations of the Convective Triggering Potential and Low-Level Humidity Index (CTP-HIlow) were computed during the 1200 UTC observation time and displayed good agreement during both 2017 and 2019 warm seasons. Radiosonde and AERI derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HIlow framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde and AERI derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HIlow. Observations of CTP-HIlow computed within a few hours of 1200 UTC were essentially equivalent, however with greater differences in time arose greater differences in CTP and HIlow.

Evaluation of the Rapid Refresh Numerical Weather Prediction Model over Arctic Alaska

AbstractDespite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

Advances in the Quality Control Methods of Air Temperature Data at Surface Automatic Weather Stations

There are errors and deviations in the meteorological data for many reasons. With the increasing scientific requirements for the quality of the meteorological data, the quality control technology of the meteorological data has been explored and developed. The air temperature is one of the routine and important factors in surface meteorological observation with one of the longest observation histories since records began. Therefore, the quality control methods for air temperature data at home and abroad are summarized in this paper. The advantages, disadvantages, and applicability of the methods are analysed.

Research on the summer meteorological support method of Yan Liang Airport for flight test

Based on the demand of the aircraft flight test of wet braking distance, wiper function and high temperature subject flight test on the meteorological conditions. The surface meteorological observations of Yan Liang Airport were used to statistically analysis the characteristics of summer precipitation and high temperature weather. Based on the demand of the flight test management, organization and command department to implement the airborne meteorological radar detection performance flight test, formulate the risk prevention plan of thunderstorm and other strong convective weather to ensure the safety of flight test. The surface meteorological observations of Yan Liang Airport and Doppler weather radar detection data of Xi’an Jing Yang Station were used to statistically analysis the characteristics of summer thunderstorm weather at Yan Liang Airport and airspace. In recent years, it has provided strong technical support to ensure the quality, efficiency, progress and safety of the flight test.

Analysis of temperature threshold in flight test

The annual, seasonal, monthly and daily distribution characteristics, as well as the occurrence and duration of air temperature at Yan Liang Airport were statistical analysis by Matlab software using surface meteorological observation air temperature data from May 2014 to May 2019. It is discussed that the average temperature threshold distribution ranges from 10-25°C in spring, 25-35°C in summer, 15-25°C in autumn and 0-15°C in winter. It showed a good negative correlation with the temperature and relative humidity and it passed the significance test. The daily difference of temperature is positively correlated with the cloud amount. When the total cloud cover is 8-10 amounts, the daily difference of temperature is about 4°C. When the total cloud cover is less than 7 amounts, the daily difference of temperature is 9-10°C. When the total cloud cover is 0-3 amounts, the daily difference of temperature is 9-10°C. Statistics founds that the lowest temperature of Yan Liang Airport in the past 5 years was -13.2 °C, and the highest temperature was 41.8 °C. The minimum temperature occurred in January and the maximum temperature occurred in July. The daily minimum temperature occurred from 05:00 to 07:00 and the daily maximum temperature occurred from 15:00 to 16:00. The corresponding conclusions can provide a scientific basis for the decision of the “window” for a certain subject of flight test. Improve the utilization of meteorological resources and the efficiency that also ensure the progress of the test flight.

Surface and Radar Observations of Winter Weather Conditions during Traffic Crashes in Northern Indiana

We examine the relationship between snow events and traffic crashes in four counties in northern Indiana across three consecutive winters, totaling 15,515 crashes. We compare daily crash occurrence and frequency with daily snowfall observations at the nearby South Bend International Airport. Then, we analyze radar and surface meteorological observations during the 3,801 crashes in the data set that were reported during “snow” or “blowing sand/soil/snow” conditions. Results show that daily crash totals increase by about 3.1 percent per centimeter of daily snowfall. Over half of crashes reported during snow or blowing sand/soil/snow conditions intersected very weak base reflectivity values from the nearby IWX radar, indicating snowfall was either very light or absent. Comparison of two different crash samples reveals that temporal clustering is most common among crashes intersecting reflectivity values >15.0 dBZ during midday and afternoon hours. Thus, we find that 15.0 dBZ is the approximate reflectivity value representing the intensity at which snowfall becomes notably more hazardous to traffic during high volumes. Finally, about a third of the crashes reported during blowing sand/soil/snow conditions that did not intersect substantial radar reflectivity occurred during meteorological observations consistent with blowing snow according to prior research.

Investigating the influence of synoptic circulation patterns on regional dry and moist heat waves in North China

Summer (June–August) heat waves in North China are found to be either primarily dry or moist, based on surface meteorological observations. This study characterizes synoptic circulation patterns (i.e., 500 hPa geopotential height) using the self-organizing map (SOM) method and investigates the influence of synoptic circulation patterns on these two types of heat waves. Results show that regional dry and moist heat waves are associated with different circulation patterns, which significantly modulate the advection of water vapor within the low-level atmosphere, and soil moisture and evaporation conditions at the surface. Regional dry heat waves are associated with times when a continental high pressure ridge is situated to the northwest of North China, and when the northern edge of the western North Pacific subtropical high (WNPSH) is south of 30° N. Regional moist heat waves are associated with a northward shift of the WNPSH. Long term variations of dry and moist heat wave occurrences correlated significantly with the occurrences of their associated circulation patterns at 0.38 (p = 0.02) and 0.71 (p = 0.00), respectively. On sub-seasonal time scales, the dominant heat wave type transforms from dry in June to moist in late July, which is in accordance with summer north–south WNPSH shifts. In addition, training the SOM with absolute geopotential height results in representative circulation patterns that are closely related to surface heat wave conditions in North China rather than the anomaly field, which mixes different circulation regimes.

GOFOS, ground optical fog observation system for monitoring the vertical stratocumulus-fog cloud distribution in the coast of the Atacama Desert, Chile

Studying fog requires a good understanding of both its horizontal and vertical distributions. Among these two, the vertical one is the most challenging to observe. Different methods have been adopted for observing this variable, among which the most commonly used are satellite datasets, together with airborne and meteorological surface observations. Yet, all these techniques present significant limitations as for spatial and temporal resolution when describing the vertical fog structure. In this manuscript, we introduce the Ground Optical Fog Observation System (GOFOS), a new method to describe the advective fog’s frequency and vertical distribution. This tool is based on in-situ optical observations, taking advantage of the topography of the Atacama Desert coastal mountains, where advective fog is frequent. During 2017, the GOFOS reported a fog presence <10% during summer and of ~50% during winter. Also, the GOFOS shows a diurnal variability of fog presence that decreases around noon and increases again starting from the afternoon. The vertical structure observed by the GOFOS shows a cloud depth of ~250 m, subject to diurnal and seasonal variabilities. By analyzing GOFOS results through a comparison with the ones obtained by standard observations, a ~93% agreement was found between the fog frequencies detected by the GOFOS and GOES. Also, we found a ~80% consistency between GOFOS fog frequency values and marine boundary layer regimes related to fog formation. Moreover, we observed that the cloud base height estimated by the GOFOS is correlated with the Iquique Airport ceilometer measurements (r = +0.6). Finally, the mean cloud top height estimated by the GOFOS differs by 8% from the Antofagasta Airport radio-soundings measurements. These results provide useful information for the selection of optimal sites for fog harvesting and for getting a better understanding of the interactions between fog and its ecosystem. In conclusion, through this study the GOFOS demonstrated its potential as a simple, reliable, and affordable method for systematic fog monitoring that might also be adapted to different topographic conditions.

XMET—An Unattended Meteorological Sensing System for Austere Environments

AbstractAccurate surface meteorological (MET) observations reported reliably and in near–real time remain a critical component of on-scene environmental observation systems. Presented is a system developed by Scripps Institution of Oceanography that allows for rapid, global deployment of ground-based weather observations to support both timely decision-making and collection of high-quality weather time series for science or military applications in austere environments. Named the Expeditionary Meteorological (XMET), these weather stations have been deployed in extreme conditions devoid of infrastructure ranging from tropical, polar, maritime, and desert environments where near continuous observations were reported. To date, over 1 million weather observations have been collected during 225 deployments around the world with a data report success rate of 99.5%. XMET had its genesis during Operation Iraqi Freedom (OIF), when the U.S. Marine Corps 3rd Marine Aircraft Wing identified an immediate capability gap in environmental monitoring of their operation area due to high spatiotemporal variability of dust storms in the region. To address the sensing gap, XMET was developed to be a portable, expendable, ruggedized, self-contained, bidirectional, weather observation station that can be quickly deployed anywhere in the world to autonomously sample and report aviation weather observations. This paper provides an overview of the XMETs design, reliability in different environments, and examples of unique meteorological events that highlight both the unit’s reliability and ability to provide quality time series. The overview shows expeditionary MET sensing systems, such as the XMET, are able to provide long-term continuous observational records in remote and austere locations essential for regional spatiotemporal MET characterization.

A dataset of sea surface meteorological observation and statistical products of Northwest Pacific

A dataset of sea surface meteorological observation and statistical products of Northwest Pacific

Estimates of Daily Evapotranspiration in the Source Region of the Yellow River Combining Visible/Near-Infrared and Microwave Remote Sensing

The spatial variation of surface net radiation, soil heat flux, sensible heat flux, and latent heat flux at different times of the day over the northern Tibetan Plateau were estimated using the Surface Energy Balance System algorithm, data from the FY-2G geostationary meteorological satellite, and microwave data from the FY-3C polar-orbiting meteorological satellite. In addition, the evaporative fraction was analyzed, and the total evapotranspiration (ET) was obtained by the effective evaporative fraction to avoid the error from accumulation. The hourly change of latent heat flux presented a sound unimodal diurnal variation. The results showed the regional ET ranged between 2.0 and 4.0 mm over the Source Region of the Yellow River. The conditional expectations of surface energy components during the experimental period of the study area were statistically analyzed, and the correspondence between different surface temperatures and the effective energy distribution was examined. The effective energy distribution of the surface changed significantly with the increase in temperature; in particular, when the surface temperature exceeded 290 K, the effective energy was mainly used for surface ET. The aim of this study was to avoid the use of surface meteorological observations that are not readily available over large areas, and the findings lay a foundation for the commercialization of land surface evapotranspiration.