DOCSLIB.ORG

Atmospheric Instability Parameters Derived from Msg Seviri Observations

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Atmospheric Instability Parameters Derived from Msg Seviri Observations P3.33 ATMOSPHERIC INSTABILITY PARAMETERS DERIVED FROM MSG SEVIRI OBSERVATIONS Marianne König*, Stephen Tjemkes, Jochen Kerkmann EUMETSAT, Darmstadt, Germany 1. INTRODUCTION K-Index: Convective systems can develop in a thermo- (Tobs(850)–Tobs(500)) + TDobs(850) – (Tobs(700)–TDobs(700)) dynamically unstable atmosphere. Such systems may quickly reach high altitudes and can cause severe Lifted Index: storms. Meteorologists are thus especially interested to Tobs - Tlifted from surface at 500 hPa identify such storm potentials when the respective system is still in a preconvective state. A number of Maximum buoyancy: obs(max betw. surface and 850) obs(min betw. 700 and 300) instability indices have been defined to describe such Qe - Qe situations. Traditionally, these indices are taken from temperature and humidity soundings by radiosondes. As (T is temperature, TD is dewpoint temperature, and Qe radiosondes are only of very limited temporal and is equivalent potential temperature, numbers 850, 700, spatial resolution there is a demand for satellite-derived 300 indicate respective hPa level in the atmosphere) indices. The Meteorological Product Extraction Facility (MPEF) for the new European Meteosat Second and the precipitable water content as additionally Generation (MSG) satellite envisages the operational derived parameter. derivation of a number of instability indices from the brightness temperatures measured by certain SEVIRI 3. DESCRIPTION OF ALGORITHMS channels (Spinning Enhanced Visible and Infrared Imager, the radiometer onboard MSG). The traditional 3.1 The Physical Retrieval physical approach to this kind of retrieval problem is to infer the atmospheric profile via a constrained inversion An iterative solution to the inversion equation (e.g. and compute the indices then directly from the obtained Ma et al. (1999)) tries to infer the temperature and profile. As this algorithm would impose a too high humidity profile from the measured brightness computer load on the MPEF system, the operational temperatures, the back-ground atmospheric profile MPEF will use a statistical approach: The measured (usually referred to as first guess) and the associated brightness temperatures together with further predictors error/noise matrices. In each iteration step, the inversion are used to derive each instability index, where the needs knowledge about the change of brightness statistical relations between these parameters are temperature with the change of the atmosphere in each gained from a neural network and appropriate training level, which is described by the Jacobians of a radiation data. Both methods are currently installed in the model. It is the computation of these Jacobians together Eumetsat MPEF prototype environment and are tested with the inversion of large matrices which make this on GOES sounder data. This paper shortly describes method very CPU intensive. This method was chosen the two methods and shows a detailed comparison for the current operational retrieval of lifted index and between the two methods and to independent precipitable water a for the GOES sounder at CIMSS radiosonde data. It should be noted that both methods (Menzel et al., 1998), and the Eumetsat prototype only only allow the derivation of instability indices over differs from the CIMSS approach by using RTTOV as cloudfree areas. the radiation model (Saunders et al., 1999). RTTOV has the advantage of a much faster computation of the 2. DEFINITION OF INSTABILITY INDICES Jacobians. In application to the GOES sounder, the algorithm uses the sounder channels 5, 7, 8, 10, 11, and Various studies have been performed to relate certain 12, which are at about 13.4 mm, 12.0 mm, 10.8 mm, 7.3 instability measures taken from radio soundings to the mm, 6.5 mm, and 6.2 mm wavelength, resp.. In a possible occurrence of severe weather. It became soon clear that future application to SEVIRI, the method will also use 6 there is no unique index for all synoptic situations and channels, and as SEVIRI will not have a 6.5 mm for all locations. This paper focuses on 4 different channel, the 8.7 mm channel will be used instead. This parameters, three classical instability indices: channel selection ensures that sufficient information concerning the atmospheric state is passed to the retrieval algorithm, e.g. surface skin temperature and * Corresponding author’s address: Marianne König, low level moisture via the two window channels, higher EUMETSAT, Postfach 100555, 64205 Darmstadt, level humidity via the water vapour channels, and the Germany, phone (49) 6151 807344, email: higher level temperature information via the 13.4 mm [email protected] channel. Over clouds, the algorithm cannot find a solution, The real problem concerned with the neural net is i.e. the iteration scheme does not converge in these to have a good training dataset: This dataset must cases. Over clear skies, convergence is usually contain a wide range of possible observations of the achieved after one or two iterations, and each instability predictors and the output value. Otherwise the net will index can then easily be derived from the atmospheric perform badly if it is faced with real data which were not profile. properly reflected in the training dataset. This problem is Tests show that the physical retrieval substantially quite evident if radiosonde data are used for training: improves the first guess data concerning the resulting Although the input values can be obtained from the instability indices, where the improvements are clearly sounding rather easily – the brightness temperatures related to the better detection of the unstable cases. can be calculated with a forward model – and the Closer inspection of the retrieved actual profiles instability index is directly inferred from the sounding, demonstrates that the retrieval scheme mostly changes the radiosondes still give a very poor training dataset. the surface skin temperature and the low level humidity Due to the only twice daily (00 and 12 UTC) soundings, profile, while it leaves the temperature profile very near the diurnal cycle is not resolved, and also spatial to the first guess values. coverage is very poor. Locations are only a fixed set of For the MSG MPEF prototyping, the results of the certain values, and the ocean areas are practically not physical method are used as a kind of reference to covered at all. It was thus decided to use the results of assess the results of the statistical approach. the physical retrieval (taken from historical data) as training data for the neural net. This provides data for 3.2 The Statistical Method every possible location within the satellite’s field of view with good diurnal coverage. Initial results with training This method is based on a neural network based on several months of GOES data showed very approach: The neural net is used to identify linear and good agreement between the two methods. non-linear relationships between a number of input For this training phase, data were collected of values – the predictors – and one output value – the satellite measured brightness temperatures, of the pixel respective instability index. In general, a neural network locations and scan times and of the respective instability is a computer model of individual elements commonly index as provided by the physical method. This large referred to as neurons. The input parameters to the dataset (about 70,000 entries of heavily sampled GOES model make up the input neurons, the output value is sounder data over several months) was randomly split then the output neuron. There can be intermediate into three categories: One third of the data was used for layers which are called hidden layers of any number of the neural network training, one third was used as the neurons. The neurons of the individual layers are so-called generalisation data within the network training, connected by links, where each link is given a certain and the third section was used as an independent weight. The inputs are processed by a weighted dataset to test the network’s performance for data summation and the transfer function passes the result to unknown to the net during its learning phase. Figure 1 the neurons of the next layer, until the output is shows an example of this initial network test for the produced. During a training phase of the neural net, the precipitable water content. For the other indices, there is weights are optimised to fit the wanted output. A neural slightly more scatter with correlation coefficients network must thus be trained with input / output pairs, between 0.85 and 0.90. i.e. with some independent data. In our case, we use a simple three-layer backpropagation neural network (e.g. Chauvin and ) 80 2 Rumelhart, 1995) with 15 input neurons and one hidden 70 layer with 20 neurons. The transfer function is the hyperbolic tangent f(x) = tanh(x). 60 A backpropagation network is trained by learning 50 with clearly defined pairs of inputs and outputs. With 40 these ‘wanted’ outputs, the neural net successively 30 adjusts its weights in every learning cycle to minimise 20 the error between the ‘wanted’ output and the output produced with the weights and the transfer function from 10 the given inputs. This training cycle is repeated for a Precipitable Water (neural net, kg/m 0 large number of input / output patterns until a minimum 0 20 40 60 80 2 error is achieved. The criterion of a minimum error is Precipitable Water (reference, kg/m ) also used to determine the characteristics of the input Figure 1: Scatter plot of neural network derived values and the number of the hidden neurons. Obvious precipitable water compared to independent input values are of course the six brightness reference data from the physical method (correlation temperatures in the six channels (see section 3.1) and coefficient 0.97) the satellite viewing angle. Eight further parameters, which give some seasonal and diurnal time information and knowledge about the geographical location, slightly improved the performance of the net.
Load more

Recommended publications
  • Impacts of Anthropogenic Aerosols on Fog in North China Plain
    BNL-209645-2018-JAAM Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Impacts of Anthropogenic Aerosols on Fog in North China Plain 10.1029/2018JD029437 Xingcan Jia1,2,3 , Jiannong Quan1 , Ziyan Zheng4, Xiange Liu5,6, Quan Liu5,6, Hui He5,6, 2 Key Points: and Yangang Liu • Aerosols strengthen and prolong fog 1 2 events in polluted environment Institute of Urban Meteorology, Chinese Meteorological Administration, Beijing, China, Brookhaven National Laboratory, • Aerosol effects are stronger on Upton, NY, USA, 3Key Laboratory of Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University microphysical properties than of Information Science and Technology, Nanjing, China, 4Institute of Atmospheric Physics, Chinese Academy of Sciences, macrophysical properties Beijing, China, 5Beijing Weather Modification Office, Beijing, China, 6Beijing Key Laboratory of Cloud, Precipitation and • Turbulence is enhanced by aerosols during fog formation and growth but Atmospheric Water Resources, Beijing, China suppressed during fog dissipation Abstract Fog poses a severe environmental problem in the North China Plain, China, which has been Supporting Information: witnessing increases in anthropogenic emission since the early 1980s. This work first uses the WRF/Chem • Supporting Information S1 model coupled with the local anthropogenic emissions to simulate and evaluate a severe fog event occurring Correspondence to: in North China Plain. Comparison of the simulations against observations shows that WRF/Chem well X. Jia and Y. Liu, reproduces the general features of temporal evolution of PM2.5 mass concentration, fog spatial distribution, [email protected]; visibility, and vertical profiles of temperature, water vapor content, and relative humidity in the planetary [email protected] boundary layer throughout the whole period of the fog event.
    [Show full text]
  • GEOGRAPHY OPTIONAL by SHAMIM ANWER
    GEOGRAPHY OPTIONAL by SHAMIM ANWER LEARNING GEOGRAPHY - A NEVER BEFORE EXPERIENCE PREP SUPPLEMENT CLIMA T OLOGY NOT FOR SALE | Off : 57/17 1st Floor, Old Rajender Nagar 8026506054, 8826506099 | Above Dr, Batra’s Delhi - 110060 Also visit us on www.keynoteias.com | www.facebook.com/keynoteias.in KEYNOTE IAS PHYSICAL GEOGRAPHY CLIMATOLOGY INDEX 1. CIRCULATION OF THE ATMOSPHERE ................................................................1-13 Local Winds; Observed Distribution of Pressure and Winds and Idealized Zonal Pressure Belts; Monsoons, Westerlies and Jet Streams; EL Nino and La Nina. 2. MOISTURE AND ATMOSPHERIC STABILITY ..................................................14-24 Atmospheric Stability and Instability 3. FORMS OF CONDENSATION AND PRECIPITATION ........................................25-38 Types and Global Distribution of Precipitation 4. AIR MASSES...........................................................................................................39-53 Polar-Front Theory; Fronts; Cyclone Formation; Cyclonic and Anticyclonic Circulation PREP-SUPPLEMENT: CLIMATOLOGY 1 1. CIRCULATION OF THE ATMOSPHERE Atmospheric circulation and wind Macroscale Winds: The largest wind patterns, called macroscale winds, are exemplified by the Winds are generated by pressure differences that westerlies and trade winds. These planetary-scale arise because of unequal heating of Earth's surface. flow patterns extend around the entire globe and Global winds are generated because the tropics can remain essentially unchanged for weeks at
    [Show full text]
  • Atmospheric Stability Atmospheric Lapse Rate
    ATMOSPHERIC STABILITY ATMOSPHERIC LAPSE RATE The atmospheric lapse rate ( ) refers to the change of an atmospheric variable with a change of altitude, the variable being temperature unless specified otherwise (such as pressure, density or humidity). While usually applied to Earth's atmosphere, the concept of lapse rate can be extended to atmospheres (if any) that exist on other planets. Lapse rates are usually expressed as the amount of temperature change associated with a specified amount of altitude change, such as 9.8 °Kelvin (K) per kilometer, 0.0098 °K per meter or the equivalent 5.4 °F per 1000 feet. If the atmospheric air cools with increasing altitude, the lapse rate may be expressed as a negative number. If the air heats with increasing altitude, the lapse rate may be expressed as a positive number. Understanding of lapse rates is important in micro-scale air pollution dispersion analysis, as well as urban noise pollution modeling, forest fire-fighting and certain aviation applications. The lapse rate is most often denoted by the Greek capital letter Gamma ( or Γ ) but not always. For example, the U.S. Standard Atmosphere uses L to denote lapse rates. A few others use the Greek lower case letter gamma ( ). Types of lapse rates There are three types of lapse rates that are used to express the rate of temperature change with a change in altitude, namely the dry adiabatic lapse rate, the wet adiabatic lapse rate and the environmental lapse rate. Dry adiabatic lapse rate Since the atmospheric pressure decreases with altitude, the volume of an air parcel expands as it rises.
    [Show full text]
  • Atmospheric Thermal and Dynamic Vertical Structures of Summer Hourly Precipitation in Jiulong of the Tibetan Plateau
    atmosphere Article Atmospheric Thermal and Dynamic Vertical Structures of Summer Hourly Precipitation in Jiulong of the Tibetan Plateau Yonglan Tang 1, Guirong Xu 1,*, Rong Wan 1, Xiaofang Wang 1, Junchao Wang 1 and Ping Li 2 1 Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China; [email protected] (Y.T.); [email protected] (R.W.); [email protected] (X.W.); [email protected] (J.W.) 2 Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China; [email protected] * Correspondence: [email protected]; Tel.: +86-27-8180-4913 Abstract: It is an important to study atmospheric thermal and dynamic vertical structures over the Tibetan Plateau (TP) and their impact on precipitation by using long-term observation at represen- tative stations. This study exhibits the observational facts of summer precipitation variation on subdiurnal scale and its atmospheric thermal and dynamic vertical structures over the TP with hourly precipitation and intensive soundings in Jiulong during 2013–2020. It is found that precipitation amount and frequency are low in the daytime and high in the nighttime, and hourly precipitation greater than 1 mm mostly occurs at nighttime. Weak precipitation during the daytime may be caused by air advection, and strong precipitation at nighttime may be closely related with air convection. Both humidity and wind speed profiles show obvious fluctuation when precipitation occurs, and the greater the precipitation intensity, the larger the fluctuation. Moreover, the fluctuation of wind speed Citation: Tang, Y.; Xu, G.; Wan, R.; Wang, X.; Wang, J.; Li, P.
    [Show full text]
  • Static Stability (I) the Concept of Stability (Ii) the Parcel Technique (A
    ATSC 5160 – supplement: static stability Static stability (i) The concept of stability The concept of (local) stability is an important one in meteorology. In general, the word stability is used to indicate a condition of equilibrium. A system is stable if it resists changes, like a ball in a depression. No matter in which direction the ball is moved over a small distance, when released it will roll back into the centre of the depression, and it will oscillate back and forth, until it eventually stalls. A ball on a hill, however, is unstably located. To some extent, a parcel of air behaves exactly like this ball. Certain processes act to make the atmosphere unstable; then the atmosphere reacts dynamically and exchanges potential energy into kinetic energy, in order to restore equilibrium. For instance, the development and evolution of extratropical fronts is believed to be no more than an atmospheric response to a destabilizing process; this process is essentially the atmospheric heating over the equatorial region and the cooling over the poles. Here, we are only concerned with static stability, i.e. no pre-existing motion is required, unlike other types of atmospheric instability, like baroclinic or symmetric instability. The restoring atmospheric motion in a statically stable atmosphere is strictly vertical. When the atmosphere is statically unstable, then any vertical departure leads to buoyancy. This buoyancy leads to vertical accelerations away from the point of origin. In the context of this chapter, stability is used interchangeably
    [Show full text]
  • Basic Features on a Skew-T Chart
    Skew-T Analysis and Stability Indices to Diagnose Severe Thunderstorm Potential Mteor 417 – Iowa State University – Week 6 Bill Gallus Basic features on a skew-T chart Moist adiabat isotherm Mixing ratio line isobar Dry adiabat Parameters that can be determined on a skew-T chart • Mixing ratio (w)– read from dew point curve • Saturation mixing ratio (ws) – read from Temp curve • Rel. Humidity = w/ws More parameters • Vapor pressure (e) – go from dew point up an isotherm to 622mb and read off the mixing ratio (but treat it as mb instead of g/kg) • Saturation vapor pressure (es)– same as above but start at temperature instead of dew point • Wet Bulb Temperature (Tw)– lift air to saturation (take temperature up dry adiabat and dew point up mixing ratio line until they meet). Then go down a moist adiabat to the starting level • Wet Bulb Potential Temperature (θw) – same as Wet Bulb Temperature but keep descending moist adiabat to 1000 mb More parameters • Potential Temperature (θ) – go down dry adiabat from temperature to 1000 mb • Equivalent Temperature (TE) – lift air to saturation and keep lifting to upper troposphere where dry adiabats and moist adiabats become parallel. Then descend a dry adiabat to the starting level. • Equivalent Potential Temperature (θE) – same as above but descend to 1000 mb. Meaning of some parameters • Wet bulb temperature is the temperature air would be cooled to if if water was evaporated into it. Can be useful for forecasting rain/snow changeover if air is dry when precipitation starts as rain. Can also give
    [Show full text]
  • P7.1 a Comparative Verification of Two “Cap” Indices in Forecasting Thunderstorms
    P7.1 A COMPARATIVE VERIFICATION OF TWO “CAP” INDICES IN FORECASTING THUNDERSTORMS David L. Keller Headquarters Air Force Weather Agency, Offutt AFB, Nebraska 1. INTRODUCTION layer parcels are then sufficiently buoyant to rise to the Level of Free Convection (LFC), resulting in convection The forecasting of non-severe and severe and possibly thunderstorms. In many cases dynamic thunderstorms in the continental United States forcing such as low-level convergence, low-level warm (CONUS) for military customers is the responsibility of advection, or positive vorticity advection provide the Air Force Weather Agency (AFWA) CONUS Severe additional force to mechanically lift boundary layer Weather Operations (CONUS OPS), and of the Storm parcels through the inversion. Prediction Center (SPC) for the civilian government. In the morning hours, one of the biggest Severe weather is defined by both of these challenges in severe weather forecasting is to organizations as the occurrence of a tornado, hail determine not only if, but also where the cap will break larger than 19 mm, wind speed of 25.7 m/s, or wind later in the day. Model forecasts help determine future damage. These agencies produce ‘outlooks’ denoting soundings. Based on the model data, severe storm areas where non-severe and severe thunderstorms are indices can be calculated that help measure the expected. Outlooks are issued for the current day, for predicted dynamic forcing, instability, and the future ‘tomorrow’ and the day following. The ‘day 1’ forecast state of the capping inversion. is normally issued 3 to 5 times per day, the ‘day 2’ and One measure of the cap is the Convective ‘day 3’ forecasts less frequently.
    [Show full text]
  • OBSERVATIONAL ANALYSIS of the PREDICTABILITY of MESOSCALE CONVECTIVE SYSTEMS by Israel L
    National Science Foundation Graduate Fellowship DGE-0234615 and Grant ATM-0324324 OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMS by Israel L. Jirak William R. Cotton, P.I. OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMS by Israel L. Jirak Department of Atmospheric Science Colorado State University Fort Collins, Colorado 80523 Research Supported by National Science Foundation under a Graduate Fellowship DGE-0234615 and Grant ATM-0324324 October 27, 2006 Atmospheric Science Paper No. 778 ABSTRACT OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMS Mesoscale convective systems (MCSs) have a large influence on the weather over the central United States during the warm season by generating essential rainfall and severe weather. To gain insight into the predictability of these systems, the precursor environment of several hundred MCSs were thoroughly studied across the U.S. during the warm seasons of 1996-98. Surface analyses were used to identify triggering mechanisms for each system, and North American Regional Reanalyses (NARR) were used to examine dozens of parameters prior to MCS development. Statistical and composite analyses of these parameters were performed to extract valuable information about the environments in which MCSs form. Similarly, environments that are unable to support organized convective systems were also carefully investigated for comparison with MCS precursor environments. The analysis of these distinct environmental conditions led to the discovery of significant differences between environments that support MCS development and those that do not support convective organization. MCSs were most commonly initiated by frontal boundaries; however, such features that enhance convective initiation are often not sufficient for MCS development, as the environment needs to lend additional support for the development and organization oflong-lived convective systems.
    [Show full text]
  • Thunderstorm Predictors and Their Forecast Skill for the Netherlands
    Atmospheric Research 67–68 (2003) 273–299 www.elsevier.com/locate/atmos Thunderstorm predictors and their forecast skill for the Netherlands Alwin J. Haklander, Aarnout Van Delden* Institute for Marine and Atmospheric Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands Accepted 28 March 2003 Abstract Thirty-two different thunderstorm predictors, derived from rawinsonde observations, have been evaluated specifically for the Netherlands. For each of the 32 thunderstorm predictors, forecast skill as a function of the chosen threshold was determined, based on at least 10280 six-hourly rawinsonde observations at De Bilt. Thunderstorm activity was monitored by the Arrival Time Difference (ATD) lightning detection and location system from the UK Met Office. Confidence was gained in the ATD data by comparing them with hourly surface observations (thunder heard) for 4015 six-hour time intervals and six different detection radii around De Bilt. As an aside, we found that a detection radius of 20 km (the distance up to which thunder can usually be heard) yielded an optimum in the correlation between the observation and the detection of lightning activity. The dichotomous predictand was chosen to be any detected lightning activity within 100 km from De Bilt during the 6 h following a rawinsonde observation. According to the comparison of ATD data with present weather data, 95.5% of the observed thunderstorms at De Bilt were also detected within 100 km. By using verification parameters such as the True Skill Statistic (TSS) and the Heidke Skill Score (Heidke), optimal thresholds and relative forecast skill for all thunderstorm predictors have been evaluated.
    [Show full text]
  • Analysis of Stability Parameters in Relation to Precipitation Associated with Pre-Monsoon Thunderstorms Over Kolkata, India
    Analysis of stability parameters in relation to precipitation associated with pre-monsoon thunderstorms over Kolkata, India H P Nayak and M Mandal∗ Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India ∗Corresponding author. e-mail: [email protected] The upper air RS/RW (Radio Sonde/Radio Wind) observations at Kolkata (22.65N, 88.45E) during pre- monsoon season March–May, 2005–2012 is used to compute some important dynamic/thermodynamic parameters and are analysed in relation to the precipitation associated with the thunderstorms over Kolkata, India. For this purpose, the pre-monsoon thunderstorms are classified as light precipitation (LP), moderate precipitation (MP) and heavy precipitation (HP) thunderstorms based on the magnitude of associated precipitation. Richardson number in non-uniformly saturated (Ri*) and saturated atmosphere (Ri); vertical shear of horizontal wind in 0–3, 0–6 and 3–7 km atmospheric layers; energy-helicity index (EHI) and vorticity generation parameter (VGP) are considered for the analysis. The instability measured ∗ in terms of Richardson number in non-uniformly saturated atmosphere (Ri ) well indicate the occurrence of thunderstorms about 2 hours in advance. Moderate vertical wind shear in lower troposphere (0–3 km) and weak shear in middle troposphere (3–7 km) leads to heavy precipitation thunderstorms. The wind shear in 3–7 km atmospheric layers, EHI and VGP are good predictors of precipitation associated with thunderstorm. Lower tropospheric wind shear and Richardson number is a poor discriminator of the three classified thunderstorms. 1. Introduction high socio-economic impact, thunderstorms are of serious concern to researchers and meteorologists.
    [Show full text]
  • Subseasonal and Diurnal Variability in Lightning and Storm Activity Over the Yangtze River Delta, China, During Mei-Yu Season
    15 JUNE 2020 Y A N G E T A L . 5013 Subseasonal and Diurnal Variability in Lightning and Storm Activity over the Yangtze River Delta, China, during Mei-yu Season a,b,c a,b d,e a,b c JI YANG, KUN ZHAO, XINGCHAO CHEN, ANNING HUANG, YUANYUAN ZHENG, AND a,b,c KANGYUAN SUN a Key Laboratory for Mesoscale Severe Weather/Ministry of Education and School of Atmospheric Science, Nanjing University, Nanjing, China b State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research of the China Meteorological Administration and Nanjing University, Beijing, China c Jiangsu Research Institute of Meteorological Sciences, Nanjing, China d Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania e Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania (Manuscript received 20 June 2019, in final form 16 March 2020) ABSTRACT Using 5 years of operational Doppler radar, cloud-to-ground (CG) lightning observations, and National Centers for Environmental Prediction reanalysis data, this study examined the spatial and temporal char- acteristics of and correlations between summer storm and lightning activity over the Yangtze River Delta (YRD), with a focus on subseasonal variability and diurnal cycles. The spatiotemporal features of storm top, duration, maximum reflectivity, size, and cell-based vertical integrated liquid water were investigated using the Storm Cell Identification and Tracking algorithm. Our results revealed that there was high storm activity over the YRD, with weak diurnal variations during the mei-yu period. Specifically, storms were strongly associated with mei-yu fronts and exhibited a moderate size, duration, and intensity.
    [Show full text]
  • Association of Rainfall and Stability Index with Lightning Parameter Over the Indo-Gangetic Plains
    American Journal of Climate Change, 2017, 6, 443-454 http://www.scirp.org/journal/ajcc ISSN Online: 2167-9509 ISSN Print: 2167-9495 Association of Rainfall and Stability Index with Lightning Parameter over the Indo-Gangetic Plains M. I. R. Tinmaker, M. Y. Aslam, D. M. Chate* Indian Institute of Tropical Meteorology, Pune, India How to cite this paper: Tinmaker, M.I.R., Abstract Aslam, M.Y. and Chate, D.M. (2017) Asso- ciation of Rainfall and Stability Index with The Lightning Imaging Sensor (LIS) based satellite lightning grid data for 10 year Lightning Parameter over the Indo-Gangetic period (1998-2007) were used to study the association of rainfall and stability in- Plains. American Journal of Climate Change, 6, 443-454. dex with lightning parameter over Indo-Gangetic plain (IGP) region. The spatial −2 −1 https://doi.org/10.4236/ajcc.2017.63023 variation of flash rate density (FRD) is found to be (40 fl·km ·yr ) higher over northern region of IGP as compare to that of eastern IGP region. The annual var- Received: July 24, 2017 iation of FRD exhibits bimodal distributions, while the precipitation rate shows Accepted: August 8, 2017 Published: August 11, 2017 unimodal distributions. The results show that the FRD peaked 2 months (pre- monsoon) in advance to the monsoon months where rainfall peak occurred due Copyright © 2017 by authors and to environmental lapse rates more than 7.0ºC/km during pre-monsoon which is Scientific Research Publishing Inc. evident from the temperature profile for correlation coefficient between temper- This work is licensed under the Creative Commons Attribution International ature (700 mb) and FRD with coefficient of 0.70, p ≤ 0.0001 during pre-monsoon.
    [Show full text]