Tropical Rainfall Measuring Mission Senior Review Proposal 2011
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Subtropical Storms in the South Atlantic Basin and Their Correlation with Australian East-Coast Cyclones
2B.5 SUBTROPICAL STORMS IN THE SOUTH ATLANTIC BASIN AND THEIR CORRELATION WITH AUSTRALIAN EAST-COAST CYCLONES Aviva J. Braun* The Pennsylvania State University, University Park, Pennsylvania 1. INTRODUCTION With the exception of warmer SST in the Tasman Sea region (0°−60°S, 25°E−170°W), the climate associated with South Atlantic ST In March 2004, a subtropical storm formed off is very similar to that associated with the coast of Brazil leading to the formation of Australian east-coast cyclones (ECC). A Hurricane Catarina. This was the first coastal mountain range lies along the east documented hurricane to ever occur in the coast of each continent: the Great Dividing South Atlantic basin. It is also the storm that Range in Australia (Fig. 1) and the Serra da has made us reconsider why tropical storms Mantiqueira in the Brazilian Highlands (Fig. 2). (TS) have never been observed in this basin The East Australia Current transports warm, although they regularly form in every other tropical water poleward in the Tasman Sea tropical ocean basin. In fact, every other basin predominantly through transient warm eddies in the world regularly sees tropical storms (Holland et al. 1987), providing a zonal except the South Atlantic. So why is the South temperature gradient important to creating a Atlantic so different? The latitudes in which TS baroclinic environment essential for ST would normally form is subject to 850-200 hPa formation. climatological shears that are far too strong for pure tropical storms (Pezza and Simmonds 2. METHODOLOGY 2006). However, subtropical storms (ST), as defined by Guishard (2006), can form in such a. -
Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling Over the Luanhe River Basin, China
water Article Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China Peizhen Ren, Jianzhu Li *, Ping Feng, Yuangang Guo and Qiushuang Ma State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China; [email protected] (P.R.); [email protected] (P.F.); [email protected] (Y.G.); [email protected] (Q.M.) * Correspondence: [email protected]; Tel.: +86-136-2215-1558 Received: 26 March 2018; Accepted: 14 May 2018; Published: 24 May 2018 Abstract: Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. -
Use of Simulated Reflectances Over Bright Desert
This article was downloaded by: [European Space Agency] On: 06 August 2014, At: 01:49 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Remote Sensing Letters Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/trsl20 Use of simulated reflectances over bright desert target as an absolute calibration reference Yves Govaerts a , Sindy Sterckx b & Stefan Adriaensen b a Govaerts Consulting , Brussels , Belgium b VITO, Centre for Remote Sensing and Earth Observation , Mol , Belgium Published online: 05 Feb 2013. To cite this article: Yves Govaerts , Sindy Sterckx & Stefan Adriaensen (2013) Use of simulated reflectances over bright desert target as an absolute calibration reference, Remote Sensing Letters, 4:6, 523-531, DOI: 10.1080/2150704X.2013.764026 To link to this article: http://dx.doi.org/10.1080/2150704X.2013.764026 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. -
(CALIPSO-PARASOL) to Evaluate Tropical Cloud Properties in the LMDZ5 GCM D
Use of A-train satellite observations (CALIPSO-PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM D. Konsta, J.-L Dufresne, H Chepfer, A Idelkadi, G Cesana To cite this version: D. Konsta, J.-L Dufresne, H Chepfer, A Idelkadi, G Cesana. Use of A-train satellite observations (CALIPSO-PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM. Climate Dynamics, Springer Verlag, 2016, 47 (3), pp.1263-1284. 10.1007/s00382-015-2900-y. hal-01384436 HAL Id: hal-01384436 https://hal.archives-ouvertes.fr/hal-01384436 Submitted on 19 Oct 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Manuscript Click here to download Manuscript: Use of A.pdf Click here to view linked References 1 Use of A-train satellite observations (CALIPSO-PARASOL) to evaluate tropical cloud properties in the LMDZ5 GCM D. Konsta, J.-L. Dufresne, H. Chepfer, A. Idelkadi, G.Cesana Laboratoire de Météorologie Dynamique (LMD/IPSL), Paris, France LMD/IPSL, UMR 8539, CNRS, Ecole Normale Supérieure, Université Pierre et Marie Curie, Ecole Polytechnique 2 3 4 5 1 6 Abstract 7 The evaluation of key cloud properties such as cloud cover, vertical profile and optical depth as well as the analysis of their 8 intercorrelation lead to greater confidence in climate change projections. -
Chlorophyll Variations Over Coastal Area of China Due to Typhoon Rananim
Indian Journal of Geo Marine Sciences Vol. 47 (04), April, 2018, pp. 804-811 Chlorophyll variations over coastal area of China due to typhoon Rananim Gui Feng & M. V. Subrahmanyam* Department of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, Zhejiang, China 316022 *[E.Mail [email protected]] Received 12 August 2016; revised 12 September 2016 Typhoon winds cause a disturbance over sea surface water in the right side of typhoon where divergence occurred, which leads to upwelling and chlorophyll maximum found after typhoon landfall. Ekman transport at the surface was computed during the typhoon period. Upwelling can be observed through lower SST and Ekman transport at the surface over the coast, and chlorophyll maximum found after typhoon landfall. This study also compared MODIS and SeaWiFS satellite data and also the chlorophyll maximum area. The chlorophyll area decreased 3% and 5.9% while typhoon passing and after landfall area increased 13% and 76% in SeaWiFS and MODIS data respectively. [Key words: chlorophyll, SST, upwelling, Ekman transport, MODIS, SeaWiFS] Introduction runoff, entrainment of riverine-mixing, Integrated Due to its unique and complex geographical Primary Production are also affected by typhoons environment, China becomes a country which has a when passing and landfall25,26,27,28&29. It is well known high frequency of natural disasters and severe that, ocean phytoplankton production (primate influence over coastal area. Since typhoon causes production) plays a considerable role in the severe damage, meteorologists and oceanographers ecosystem. Primary production can be indexed by have studied on the cause and influence of typhoon chlorophyll concentration. The spatial and temporal for a long time. -
Quickbird (Digital Globe) Scene Over Purdue Campus, 0.6M
QuickBird (Digital Globe) scene over Purdue campus, 0.6m GSD EROS‐A (ImageSat Int’l) scene over Purdue campus, GSD 2m Example of radiometry outside design limits – specular reflections from car windshields cause saturation and corruption of surrounding pixel data. Quickbird scene over Indianapolis qb_eph_short.txt satId = "QB02"; revNumber = 6984; stripId = "018F24"; type = "R"; version = "A"; generationTime = 2003-01-16T19:58:19.000000Z; startTime = 2003-01-15T16:30:25.613302Z; numPoints = 1536; timeInterval = 0.020; ephemList = ( ( 1, 408384.8844293977000000, -5163749.7635908620000000, 4436609.3965829052000000, -1239.4888535016760000, -5028.7879607649920000, -5720.9589832489428000, 0.0047847180901878, 0.0001857647065552, -0.0004488663884886, 0.0104184982125138, 0.0034951595622186, 0.0033038605521562), ( 2, 408360.0945991199100000, -5163850.3368877340000000, 4436494.9775853315000000, -1239.5137702293241000, -5028.6552395191966000, -5721.0706890709816000, 0.0047847180901878, 0.0001857647065552, -0.0004488663884886, 0.0104184982125138, 0.0034951595622186, 0.0033038605521562), ( 3, 408335.3042634393800000, -5163950.9075712236000000, 4436380.5563067487000000, -1239.5386860655569000, -5028.5225194834038000, -5721.1823886786469000, 0.0047847180901878, 0.0001857647065552, -0.0004488663884886, 0.0104184982125138, 0.0034951595622186, 0.0033038605521562), ( 4, 408310.5134390606000000, -5164051.4755447917000000, 4436266.1328579923000000, -1239.5636011559900000, -5028.3897989066654000, -5721.2940835802046000, 0.0047847180901878, 0.0001857647065552, -
Satellite Contributions to the Quantitative Characterization of Biomass Burning for Climate Modeling
Revised Version of Review Article for Atmospheric Research Satellite Contributions to the Quantitative Characterization of Biomass Burning for Climate Modeling Charles Ichoku, Ralph Kahn, Mian Chin Lab. for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA Corresponding Author: Dr. Charles Ichoku Climate & Radiation Branch, Code 613.2 NASA Goddard Space Flight Center Greenbelt, MD 20771, USA Phone : +1-301-614-6212 Fax : +1-301-614-6307 or +1-301-614-6420 Email : [email protected] Abstract Characterization of biomass burning from space has been the subject of an extensive body of literature published over the last few decades. Given the importance of this topic, we review how satellite observations contribute toward improving the representation of biomass burning quantitatively in climate and air-quality modeling and assessment. Satellite observations related to biomass burning may be classified into five broad categories: (i) active fire location and energy release, (ii) burned areas and burn severity, (iii) smoke plume physical disposition, (iv) aerosol distribution and particle properties, and (v) trace gas concentrations. Each of these categories involves multiple parameters used in characterizing specific aspects of the biomass-burning phenomenon. Some of the parameters are merely qualitative, whereas others are quantitative, although all are essential for improving the scientific understanding of the overall distribution (both spatial and temporal) and impacts of biomass burning. Some of the qualitative satellite datasets, such as fire locations, aerosol index, and gas estimates have fairly long-term records. They date back as far as the 1970s, following the launches of the DMSP, Landsat, NOAA, and Nimbus series of earth observation satellites. -
Air-Sea Interaction: Hurricane/Typhoon
Air-sea interaction: Hurricane/Typhoon ATM2106 Typhoon Haiyan, November 2013 Typhoon Vongfong, October 2014 https://youtu.be/WjxZd7fPSVI Movie from https://svs.gsfc.nasa.gov/12772 Hurricane • An intense storm of tropical origin • Winds exceeding 64 knots (74 mph, 119 km/h) Typhoon Cyclone Hurricane Tropical cyclone Hurricane structure Northern hemisphere (relatively) dry air eyewall 18 km Moist air Hurricane tour North Northern hemisphere eyewall West East N W E S South Hurricane Katrina over the Gulf of Mexico on August 28, 2005 Wind Precipitation rate 148 km/h Hurricane Formation • Hurricanes form over tropical waters where • The winds are light • The humidity is high • The surface temperature is warm (>26.5 ℃) • A convergence of the air and a force for the spin • Usually between 5° and 20° where f≠0 Sea surface temperature Number of storms Hurricane Formation • Conditions that prohibit the hurricane formation • Sinking air: near 20°, the air is open sinking associated with the subtropical high. • Strong upper-level winds: strong wind shear tends to disrupt organized convection and disperses heat and moisture. Hurricane Development 1. Sensible heat flux and latent heat release 2. Warmer T aloft near the cluster of thunderstorms 3. Pressure gradient forcing outward 4. A drop of the surface pressure from the warming and diverging the air in the small surface area. 5. The air begins to spin counterclockwise in the northern hemisphere and moves to the center 6. As the area of the circulation decreases, the wind speed increases. Hurricane Development • Hurricanes need energy to develop • Energy sources • Sensible heat from the ocean Q = ⇢ c c u (SST T ) S air p S 10 − air • Latent heat from condensation Q = ⇢ L c u (q⇤(SST) q ) L air e L 10 − air Hurricane Development Q = ⇢ c c u (SST T ) S air p S 10 − air Q = ⇢ L c u (q⇤(SST) q ) L air e L 10 − air • The warmer the water, the greater the transfer of sensible and latent heat into the air above • Greater wind speed promotes higher sensible and latent heat flux from the ocean to the atmosphere. -
PARASOL/GRASP No Location Specific Assumptions!!! Bodélé Depression AOD(565 Nm) Scale Height (M) Sensitivity Test for Aerosol Vertical Information Retrieval
GRASP aerosol from POLDER, 3MI, etc. polarimeters: towards estimation PM2.5 Oleg Dubovik1, Anton Lopatin1, Pavel Litvinov2, Yevgeny Derimian1, Tatyana Lapyonok1, Anton Lopatin1, David Fuertes2, Fabrice Ducos1, Xin Huang1, Benjamin Torres2, Michael Aspsetsberger3 and Christian Federspiel3 1 - Laboratoire d’Optique Atmosphérique, CNRS – Université Lille 1, France; 2 - GRASP-SAS, LOA, Université Lille 1, Villeneuve d’Ascq, France 3 - Catalysts GmbH, High Performance Computing, Linz, Austria AC-VC-13, Satellite aerosol for AQ, CNES, Paris, France, 29 June, 2017 Strength of GRASP algorithm concept: Based on accurate rigorous physics and math; Versatile (applicable to different sensors and retrieval of different parameters); Designed for multi-sensor retrieval (satellite, ground- based, airborne; polar and geostationary, ); Not-stagnant (different concept can be tested and compared within algorithm); Flexible: - generalizable (to IR, hypo spectral, to retrieval of gases and clouds, etc.); - or degradable (to less accurate but fast solution, LUT,…); Practical (rather fast and easy to use for given level fundamental complexity); Current and potential applications: Satellite instruments: polar: POLDER/PARASOL, 3MI/MetOp-SG, MERIS/Envisat, Sentinel-3 (OLCI, SLSTR), etc. geostationary: Sentinel-4, FCI, GOCO, Himawari-8, etc. Ground-based, airborne and laboratory instruments: passive: AERONET radiometers, sun/luna/star-photometers, etc. active: multi-wavelength elastic and non-elastic lidars; airborne and laboratory: polar nephelometers, Multi-instrument synergy: ground-based: lidar + radiometers + photometers , sun/luna/star-photometers, etc. satellite: OLCI + SLSTR, polarimeter + lidar (e.g. PARASOL + CALIPSO) Support: CNES (ТOSCA, RD), ANR (CaPPA), ESA (S-4, MERIS/S-3, GPGPU, CCI, CCI-2,CC+); EUMETSAT (3MI NRT), FP6-7 (ACTRIS 1-2), Catalysts GmbH, etc. -
White Papers
WHITE PAPERS - 13 - INCREASED SCIENTIFIC EVIDENCE FOR THE LINK OF CLIMATE CHANGE TO HURRICANE INTENSITY Christoph Bals Germanwatch ntil recently, most scientists would have said that there was no or no clear evidence that global warming has had any effect on the planet’s most powerful storms- dubbed hurricanes, typhoons, or cyclones depending on the ocean that spawns them. They would have argued that the changes of the past decade in U these metrics are not so large as to clearly indicate that anything is going on other than the multi-decadal variability that has been well documented since at least 1900 (Gray et al. 1997; Landsea et al. 1999; Goldenberg et al. 2001). 2005 a Turning Point of the Debate? 2005 might prove as a turning point of the debate. Two developments came accomponied. First, the two extreme hurricane years 2004 and 2005 increased attention of scientists: At the latest when Wilma's internal pressure hit 882 millibars, beating a record held by 1988's Gilbert, climatologists took notice. It was the first time a single season had produced four Category 5 hurricanes, the highest stage on the 5-step Saffir-Simpson scale of storm intensity. The 28 tropical storms and hurricanes, that the world faced in 2005, crushed the old mark of 21, set in 1933. Second, a number of new scientific studies provided much more support to the hypotheses by showing that „now ... a connection is emerging between warming oceans and severe tropical cyclones " (Kerr, 2005, 1807). Two papers published in Science and Nature in 2005 started a development described as “Birth for Hurricane Climatology” (Kerr, 2006) by identifying the impact of climate change on hurricane intensity, number and regional distribution. -
Principles of Active Remote Sensing: Radar. Radar Sensing of Clouds and Precipitation. Objectives: 1
Lecture 13. Principles of active remote sensing: Radar. Radar sensing of clouds and precipitation. Objectives: 1. Radar basics. Main types of radars. 2. Basic antenna parameters. 3. Particle backscattering and radar equation. 4. Sensing precipitation and clouds with ground-based and space-borne radars (weather radars, TRMM, and CloudSat). Required reading: S: 8.1, p.401-402, 5.7, 8.2.1, 8.2.2, 8.2.3, 8.3 Additional/advanced reading: Tutorials on ground-based weather radars: http://www.srh.noaa.gov/srh/jetstream/doppler/doppler_intro.htm http://www.weathertap.com/guides/radar/weather-radar-tutorial.html Tropical Rainfall Measuring Mission (TRMM) web site: http://trmm.gsfc.nasa.gov/ http://www.eorc.jaxa.jp/en/hatoyama/satellite/satdata/trmm_e.html Liu, Zhong, Dana Ostrenga, William Teng, Steven Kempler, 2012: Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications. Bull. Amer. Meteor. Soc., 93, 1317–1325. CloudSat web site: http://cloudsat.atmos.colostate.edu/ CloudSat Data Center: http://www.cloudsat.cira.colostate.edu/ 1 1. Radar basics. Main types of radars. Radar is an active remote sensing system operating at the microwave wavelength. Radar is a ranging instrument: (RAdio Detection And Ranging) Basic principles: The sensor transmits a microwave (radio) signal towards a target and detects the backscattered radiation. The strength of the backscattered signal is measured to discriminate between different targets and the time delay between the transmitted and reflected signals determines the distance (or range) to the target. Two primary advantages of radars: all-weather and day /night imaging Radar modes of operation: Constant wave (CW) mode: continuous beam of electromagnetic radiation is transmitted and received => provides information about the path integrated backscattering radiation Pulsed mode: transmits short pulses (typically 10-6-10-8 s) and measures backscattering radiation (also called echoes) as a function of range. -
Metropolitanization and Forest Recovery in Santa Catarina, Southern Brazil: a Multiscale Analysis
Metropolitanization and forest recovery in Santa Catarina, southern Brazil: a multiscale analysis Sandra R. Baptista Postdoctoral Research Fellow The Earth Institute at Columbia University Center for International Earth Science Information Network (CIESIN) 2009 Latin American IALE Conference Landscape Ecology in Latin America: Challenges and Perspectives Ecologia de Paisagens na América Latina: Desafios e Perspectivas Symp. 13 – Landscape change and forest transition theory: towards a multiscale approach October 4-7, 2009 Campos do Jordão, São Paulo, Brazil Introduction A multiscale analysis of anthropogenic landscape dynamics in the Florianópolis city-region, Santa Catarina, Brazil Anthropogenic Landscape Heterogeneity Human modification of landscapes and ecosystems within the city-region Photos by S. Baptista http://sedac.ciesin.columbia.edu/gpw/ Gridded Population of the World, version 3 (GPWv3) and the Global Rural- Urban Mapping Project (GRUMP) are the latest developments in the rendering of human populations in a common geo-referenced framework, produced by the Center for International Earth Science Information Network (CIESIN) of the Earth Institute at Columbia University. Atlantic Forest remnants in Brazil, Santa Catarina, and the Florianópolis Metro Region in 2005 Source: Adapted and translated from maps available online from Fundação SOS Mata Atlântica, URL: http://www.sosma.org.br Percent forest cover in Santa Catarina’s 293 municipalities, 2005 Remote sensing data reported by Fundação SOS Mata Atlântica and INPE Florianópolis