Evaluation of convective cloud microphysics in numerical weather predictionmodel with dual-wavelength polarimetric radar observations

<p>The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty until today. To evaluate the influence of cloud microphysics parameterizations on numerical weather prediction, a convection permitting regional weather model setup has been established using 5 different microphysics schemes of varying complexity (double-moment, spectral bin, particle property prediction (P3)). A polarimetric radar forward operator (CR-SIM) has been applied to simulate radar signals consistent with the simulated particles. The performance of the microphysics schemes is analyzed through a statistical comparison of the simulated radar signals to radar measurements on a dataset of 30 convection days.</p> <p>The observational data basis is provided by two polarimetric research radar systems in the area of Munich, Germany, at C- and Ka-band frequencies and a complementary third polarimetric C-band radar operated by the German Weather Service. By measuring at two different frequencies, the<br />dual-wavelength ratio is derived that facilitates the investigation of the particle size evolution. Polarimetric radars provide in-cloud information about hydrometeor type and asphericity by measuring, e.g., the differential reflectivity ZDR.</p> <p>Within the DFG Priority Programme 2115 PROM, we compare the simulated polarimetric and dual-wavelength radar signals with radar observations of convective clouds. Deviations are found between the schemes and observations in ice and liquid phase, related to the treatment of particle size distributions. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions. Comparison of polarimetric radar signatures reveal issues of all schemes except the spectral bin scheme to correctly represent rain particle size distributions. The polarimetric information is further exploited by applying a hydrometeor classification algorithm to obtain dominant hydrometeor classes. By comparing the simulated and observed distribution of hydrometeors, as well as the frequency, intensity and area of high impact weather situations (e.g., hail or heavy convective precipitation), the influence of cloud microphysics on the ability to correctly predict high impact weather situations is examined.</p>

Repräsentierung von Mischphasenwolken im spektralen gekoppelten Wolkenmikrophysikmodells COSMO-SPECS und Vergleich mit Fernerkundungsbeobachtungen

<p>Während der Feldkampagnen CyCyare (Limassol, Zypern) und DACAPO-PESO (Punta Arenas, Chile) wurden Fernerkundungsmethoden zur Untersuchung von Mischphasenwolken eingesetzt. Die beiden Standorte zeigen unterschiedliche Aerosolbelastungen mit sehr sauberen, marinen Luftmassen über dem Süden Chiles und höheren Aerosolmassen- und -anzahlkonzentrationen über Zypern, die häufig staubbelastet sind. Die Beobachtungen deuten auf unterschiedliche Wolkeneigenschaften hin. Um die Eigenschaften und die Entwicklung der beobachteten Wolken sowie ihre Beziehung zum umgebenden Aerosol weiter zu untersuchen, wurde das detaillierte gekoppelte mikrophysikalische Wolkenmodell COSMO-SPECS für ausgewählte reale Fallstudien angewendet.</p> <p>Das SPECtral bin cloud microphysicS Modell SPECS wurde entwickelt, um Wolkenprozesse unter Verwendung von fixed-bin Größenverteilungen von Aerosolpartikeln und von flüssigen und gefrorenen Hydrometeoren zu simulieren. Es wurde in das numerische Wettervorhersagemodell COSMO implementiert und ersetzt dort die vorhandene Wolkenmikrophysik. COSMO-SPECS wurde bisher für idealisierte Fallstudien mit horizontal periodischen Randbedingungen verwendet. Mit der Berücksichtigung seitlicher Randbedingungen für die Hydrometerspektren können nun auch hochauflösende reale Fallstudien auf genesteten Gittern durchgeführt werden. Dabei wird der meteorologische Treiber COSMO mit seiner Standard-Zweimoment-Wolkenmikrophysik in mehreren Schritten auf immer feineren Gittern mit zunehmender horizontaler Auflösung angewendet. Schließlich wird das COSMO-SPECS-Modellsystem auf den innersten Gebiet mit einer horizontalen Auflösung von einigen hundert Metern angewandt, wobei Randbedingung verwendet werden, die aus dem feinsten antreibenden COSMO-Gebiet stammen. Zu diesem Zweck müssen die nicht-größenaufgelösten Hydrometeorfelder des antreibenden Modells in die entsprechenden Hydrometormassen- und -anzahlverteilungen der Hydrometerspektren von SPECS übersetzt werden.</p> <p>In dieser Arbeit präsentieren wir Ergebnisse für ausgewählte Fallstudien von Mischphasenwolken, die während CyCyare und DACAPO-PESO beobachtet wurden. Mit einer Reihe von Modellsimulationen wurde die Abhängigkeit der resultierenden Wolkeneigenschaften und der Niederschlagsbildung von der INP- und Aerosolkonzentration sowie spezifischer mikrophysikalischer Prozesse untersucht. Die Ergebnisse der Modellsimulationen wurden mit den vorhandenen LIDAR- und Wolkenradarbeobachtungen an den beiden Standorten verglichen.</p>

Environmental Response in Coupled Energy and Water Cloud Impact Parameters Derived from A-Train Satellite, ERA-Interim and MERRA-2

Understanding the connections between latent heating from precipitation and cloud radiative effects is essential for accurately parameterizing cross-scale links between cloud microphysics and global energy and water cycles in climate models. While commonly examined separately, this study adopts two cloud impact parameters (CIPs), the surface radiative cooling efficiency, Rc, and atmospheric radiative heating efficiency, Rh, that explicitly couple cloud radiative effects and precipitation to characterize how efficiently precipitating cloud systems influence the energy budget and water cycle using A-Train observations and two reanalyses. These CIPs exhibit distinct global distributions that suggest cloud energy and water cycle coupling are highly dependent on cloud regime. The dynamic regime (ω500) controls the sign of Rh, while column water vapor (CWV) appears to be the larger control on the magnitude. The magnitude of Rc is highly coupled to the dynamic regime. Observations show that clouds cool the surface very efficiently per unit rainfall at both low and high sea surface temperature (SST) and CWV, but reanalyses only capture the former. Reanalyses fail to simulate strong Rh and moderate Rc in deep convection environments but produce stronger Rc and Rh than observations in shallow, warm rain systems in marine stratocumulus regions. While reanalyses generate fairly similar climatologies in the frequency of environmental states, the response of Rc and Rh to SST and CWV results in systematic differences in zonal and meridional gradients of cloud atmospheric heating and surface cooling relative to A-Train observations that may have significant implications for global circulations and cloud feedbacks.

Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing

Multi-year ground-based remote-sensing datasets were acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites. A highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern midlatitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets in key regions of aerosol–cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary layer coupling, and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds, our study contributes the following new aspects: (1) sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled conditions is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above -15∘C than indicated by prior lidar-only studies at all sites. A virtual lidar detection threshold of ice water content (IWC) needs to be considered in order to bring radar–lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than decoupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below -15∘C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity wave influences, Punta Arenas shows lower fractions of ice-containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and -8∘C. These differences are potentially caused by the contrast in the ice-nucleating particle (INP) reservoir between the different sites.

Optimization of physical schemes in WRF model on cyclone simulations over Bay of Bengal using one-way ANOVA and Tukey’s test

Evaluation of appropriate physics parameterization schemes for the Weather Research and Forecasting (WRF) model is vital for accurately forecasting tropical cyclones. Three cyclones Nargis, Titli and Fani have been chosen to investigate the combination of five cloud microphysics (MP), three cumulus convection (CC), and two planetary boundary layer (PBL) schemes of the WRF model (ver. 4.0) with ARW core with respect to track and intensity to determine an optimal combination of these physical schemes. The initial and boundary conditions for sensitivity experiments are drawn from the National Centers for Environmental Prediction (NCEP) global forecasting system (GFS) data. Simulated track and intensity of three cyclonic cases are compared with the India Meteorological Department (IMD) observations. One-way analysis of variance (ANOVA) is applied to check the significance of the data obtained from the model. Further, Tukey’s test is applied for post-hoc analysis in order to identify the cluster of treatments close to IMD observations for all three cyclones. Results are obtained through the statistical analysis; average root means square error (RMSE) of intensity throughout the cyclone period and time error at landfall with the step-by-step elimination method. Through the elimination method, the optimal scheme combination is obtained. The YSU planetary boundary layer with Kain–Fritsch cumulus convection and Ferrier microphysics scheme combination is identified as an optimal combination in this study for the forecasting of tropical cyclones over the Bay of Bengal.

CAPE - A link amid thermodynamics and microphysics for the occurrence of severe thunderstorms

Lkkj & bl 'kks/k&i= dk mÌs’; dksydkrk ¼22°32¢] 88°20¢½ esa ekulwu iwoZ _rq ¼vizSy&ebZ½ ds nkSjku xtZ ds lkFk vkus okys Hkh"k.k rwQkuksa dh mRifÙk vkSj fodkl esa lgk;d es?k dh lw{e HkkSfrdh; izfØ;kvksa dh tk¡p djuk gSA bl v/;;u ls ;g irk pyk gS fd dksydkrk esa ekulwu&iwoZ _rq ds nkSjku xtZ ds lkFk vkus okys Hkh"k.k rwQkuksa ds nkSjku rkixfrdh;] xfrdh;] es?k dh lw{e HkkSfrdh vkSj fctyh pdeus dks J`a[kykc) djus esa laoguh; miyC/k foHko ÅtkZ ¼lh- ,- ih- bZ-½ lgk;d gSA bl v/;;u ls izkIr gq, ifj.kkeksa ls ;g irk pyk gS fd dksydkrk esa laoguh; miyC/k foHko ÅtkZ 1000 twYl izfr fd- xzk- ds Hkhrj izcy ikbZ xbZ tks eqDr laogu Lrj ¼,y- ,Q- lh-½ ls Åij fu/kkZfjr nkc Lrjksa ds Hkhrj ikbZ xbZ vkSj ok;q dh viMªk¶V xfr ds ln`’k eku fu"izHkkoh mRIykodrk Lrj ¼,y- ,u- ch-½ esa yxHkx 30 - 50 eh-@ lsdsaM ik, x,A bl v/;;u ls ;g Hkh irk pyk gS fd 5 fe- eh- rd ds O;kl ds vkdkj dh c¡wns fLFkj jg ldrh gS ftlds ckn vkdkj c<+us ds dkj.k cw¡nsa VwV tkrh gSaA tc cw¡n dh f=T;k 2-5 fe- eh- ls 3 fe- eh- dh ifjf/k esa gksrh gS rc cw¡nksa dk VwVuk 'kq: gks tkrk gS vkSj 3 fe- eh- ls 5 fe- eh- dh ifjf/k esa cw¡nksa ds VwVus dh laHkkouk vf/kd gksrh gS D;ksfd bl fLFkfr esa cw¡nksa ds yxkrkj VwVus dh dkj.k mudk thoudky cgqr NksVk gks tkrk gSA The aim of the present paper is to view the cloud microphysical processes entailed in the genesis and the development of the severe thunderstorms of pre-monsoon season (April - May) over Kolkata (22°32', 88°20'). The study shows that Convective Available Potential Energy (CAPE) is instrumental in establishing a linkage among thermodynamics, dynamics, cloud microphysics, and lightning during severe thunderstorm of pre monsoon season over Kolkata. The results of the present study reveal that for the thunderstorms reported over Kolkata, CAPE are found to be predominantly within 1000 joules per kgs within the prescribed pressure levels above the Level of Free Convection (LFC) and the corresponding values of the updraft speeds of the air are found to be nearly 30 - 50 m/s at the Level of Neutral Buoyancy (LNB). The study also depicts that the drops may grow up to the size of 5mm in diameter stably, beyond which, they tend to breakup due to the large drop instability. The breakup or splitting is observed to initiate when the drop radius is within the range of 2.5mm to 3mm and the breakup is most likely within the range of 3mm to 5mm because at this stage the lifetime of the drops are short due to the spontaneous breakup.

Spectral analysis of climate dynamics with operator-theoretic approaches

The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds in cloud microphysics, to thousands of kilometers and centuries in ocean dynamics. Yet, despite this dynamical complexity, climate dynamics is known to exhibit coherent modes of variability. A primary example is the El Niño Southern Oscillation (ENSO), the dominant mode of interannual (3–5 yr) variability in the climate system. The objective and robust characterization of this and other important phenomena presents a long-standing challenge in Earth system science, the resolution of which would lead to improved scientific understanding and prediction of climate dynamics, as well as assessment of their impacts on human and natural systems. Here, we show that the spectral theory of dynamical systems, combined with techniques from data science, provides an effective means for extracting coherent modes of climate variability from high-dimensional model and observational data, requiring no frequency prefiltering, but recovering multiple timescales and their interactions. Lifecycle composites of ENSO are shown to improve upon results from conventional indices in terms of dynamical consistency and physical interpretability. In addition, the role of combination modes between ENSO and the annual cycle in ENSO diversity is elucidated.