Cira Annual Report Fy 01/02
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Off-Line Algorithm for Calculation of Vertical Tracer Transport in The
EGU Journal Logos (RGB) Open Access Open Access Open Access Advances in Annales Nonlinear Processes Geosciences Geophysicae in Geophysics Open Access Open Access Natural Hazards Natural Hazards and Earth System and Earth System Sciences Sciences Discussions Open Access Open Access Atmos. Chem. Phys., 13, 1093–1114, 2013 Atmospheric Atmospheric www.atmos-chem-phys.net/13/1093/2013/ doi:10.5194/acp-13-1093-2013 Chemistry Chemistry © Author(s) 2013. CC Attribution 3.0 License. and Physics and Physics Discussions Open Access Open Access Atmospheric Atmospheric Measurement Measurement Techniques Techniques Off-line algorithm for calculation of vertical tracer transport in the Discussions Open Access troposphere due to deep convection Open Access Biogeosciences 1,2 1 3,4,5 6 7 8 Biogeosciences9 10 D. A. Belikov , S. Maksyutov , M. Krol , A. Fraser , M. Rigby , H. Bian , A. Agusti-Panareda , D. Bergmann , Discussions P. Bousquet11, P. Cameron-Smith10, M. P. Chipperfield12, A. Fortems-Cheiney11, E. Gloor12, K. Haynes13,14, P. Hess15, S. Houweling3,4, S. R. Kawa8, R. M. Law14, Z. Loh14, L. Meng16, P. I. Palmer6, P. K. Patra17, R. G. Prinn18, R. Saito17, 12 Open Access and C. Wilson Open Access 1Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa,Climate Tsukuba, Ibaraki, Climate 305-8506, Japan of the Past 2 of the Past Division for Polar Research, National Institute of Polar Research, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan Discussions 3SRON Netherlands Institute for Space Research, Sorbonnelaan -
Chapter 6: Ensemble Forecasting and Atmospheric Predictability
Chapter 6: Ensemble Forecasting and Atmospheric Predictability Introduction Deterministic Chaos (what!?) In 1951 Charney indicated that forecast skill would break down, but he attributed it to model errors and errors in the initial conditions… In the 1960’s the forecasts were skillful for only one day or so. Statistical prediction was equal or better than dynamical predictions, Like it was until now for ENSO predictions! Lorenz wanted to show that statistical prediction could not match prediction with a nonlinear model for the Tokyo (1960) NWP conference So, he tried to find a model that was not periodic (otherwise stats would win!) He programmed in machine language on a 4K memory, 60 ops/sec Royal McBee computer He developed a low-order model (12 d.o.f) and changed the parameters and eventually found a nonperiodic solution Printed results with 3 significant digits (plenty!) Tried to reproduce results, went for a coffee and OOPS! Lorenz (1963) discovered that even with a perfect model and almost perfect initial conditions the forecast loses all skill in a finite time interval: “A butterfly in Brazil can change the forecast in Texas after one or two weeks”. In the 1960’s this was only of academic interest: forecasts were useless in two days Now, we are getting closer to the 2 week limit of predictability, and we have to extract the maximum information Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability (chaos) b) Stable systems are infinitely predictable a) Unstable dynamical system b) Stable dynamical -
Verification of a Multimodel Storm Surge Ensemble Around New York City and Long Island for the Cool Season
922 WEATHERANDFORECASTING V OLUME 26 Verification of a Multimodel Storm Surge Ensemble around New York City and Long Island for the Cool Season TOM DI LIBERTO * AND BRIAN A. C OLLE School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York NICKITAS GEORGAS AND ALAN F. B LUMBERG Stevens Institute of Technology, Hoboken, New Jersey ARTHUR A. T AYLOR Meteorological Development Laboratory, NOAA/NWS, Office of Science and Technology, Silver Spring, Maryland (Manuscript received 21 November 2010, in final form 27 June 2011) ABSTRACT Three real-time storm surge forecasting systems [the eight-member Stony Brook ensemble (SBSS), the Stevens Institute of Technology’s New York Harbor Observing and Prediction System (SIT-NYHOPS), and the NOAA Extratropical Storm Surge (NOAA-ET) model] are verified for 74 available days during the 2007–08 and 2008–09 cool seasons for five stations around the New York City–Long Island region. For the raw storm surge forecasts, the SIT-NYHOPS model has the lowest root-mean-square errors (RMSEs) on average, while the NOAA-ET has the largest RMSEs after hour 24 as a result of a relatively large negative surge bias. The SIT-NYHOPS and SBSS also have a slight negative surge bias after hour 24. Many of the underpredicted surges in the SBSS ensemble are associated with large waves at an offshore buoy, thus illustrating the potential importance of nearshore wave breaking (radiation stresses) on the surge pre- dictions. A bias correction using the last 5 days of predictions (BC) removes most of the surge bias in the NOAA-ET model, with the NOAA-ET-BC having a similar level of accuracy as the SIT-NYHOPS-BC for positive surges. -
Seasonal and Diurnal Performance of Daily Forecasts with WRF V3.8.1 Over the United Arab Emirates
Geosci. Model Dev., 14, 1615–1637, 2021 https://doi.org/10.5194/gmd-14-1615-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Seasonal and diurnal performance of daily forecasts with WRF V3.8.1 over the United Arab Emirates Oliver Branch1, Thomas Schwitalla1, Marouane Temimi2, Ricardo Fonseca3, Narendra Nelli3, Michael Weston3, Josipa Milovac4, and Volker Wulfmeyer1 1Institute of Physics and Meteorology, University of Hohenheim, 70593 Stuttgart, Germany 2Department of Civil, Environmental, and Ocean Engineering (CEOE), Stevens Institute of Technology, New Jersey, USA 3Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates 4Meteorology Group, Instituto de Física de Cantabria, CSIC-University of Cantabria, Santander, Spain Correspondence: Oliver Branch ([email protected]) Received: 19 June 2020 – Discussion started: 1 September 2020 Revised: 10 February 2021 – Accepted: 11 February 2021 – Published: 19 March 2021 Abstract. Effective numerical weather forecasting is vital in T2 m bias and UV10 m bias, which may indicate issues in sim- arid regions like the United Arab Emirates (UAE) where ex- ulation of the daytime sea breeze. TD2 m biases tend to be treme events like heat waves, flash floods, and dust storms are more independent. severe. Hence, accurate forecasting of quantities like surface Studies such as these are vital for accurate assessment of temperatures and humidity is very important. To date, there WRF nowcasting performance and to identify model defi- have been few seasonal-to-annual scale verification studies ciencies. By combining sensitivity tests, process, and obser- with WRF at high spatial and temporal resolution. vational studies with seasonal verification, we can further im- This study employs a convection-permitting scale (2.7 km prove forecasting systems for the UAE. -
Model Error in Weather and Climate Forecasting £ Myles Allen £ , Jamie Kettleborough† and David Stainforth
Model error in weather and climate forecasting £ Myles Allen £ , Jamie Kettleborough† and David Stainforth Department£ of Physics, University of Oxford †Space Science and Technology Department, Rutherford Appleton Laboratory “As if someone were to buy several copies of the morning newspaper to assure himself that what it said was true” Ludwig Wittgenstein 1 Introduction We review various interpretations of the phrase “model error”, choosing to focus here on how to quantify and minimise the cumulative effect of model “imperfections” that either have not been eliminated because of incomplete observations/understanding or cannot be eliminated because they are intrinsic to the model’s structure. We will not provide a recipe for eliminating these imperfections, but rather some ideas on how to live with them because, no matter how clever model-developers, or fast super-computers, become, these imperfections will always be with us and represent the hardest source of uncertainty to quantify in a weather or climate forecast. We spend a little time reviewing how uncertainty in the current state of the atmosphere/ocean system is used in the initialisation of ensemble weather or seasonal forecasting, because it might appear to provide a reasonable starting point for the treatment of model error. This turns out not to be the case, because of the fundamental problem of the lack of an objective metric or norm for model error: we have no way of measuring the distance between two models or model-versions in terms of their input parameters or structure in all but a trivial and irrelevant subset of cases. Hence there is no way of allowing for model error by sampling the space of “all possible models” in a representative way, because distance within this space is undefinable. -
Touching the Clouds Activity Guide
Touching the Clouds Activity Guide Purpose Provide a mental representation of each cloud type Create a tactile cloud identification chart Overview Individuals will construct and touch a tactile model of common types of clouds to learn how to describe the clouds based on their shape and texture. They will compare their descriptions with the standard classifications using the cloud types identified in the GLOBE Clouds Protocol. Time: 45 minutes to 1 ½ hours, depending on individual’s age Level: All Materials (per person) One large sheet of cardstock (18” x 12”) Tape One set of Braille labels for each cloud type and/or markers One small feather A layered piece of blanket or soft fabric (eight 1’ X 1” pieces) Cotton balls of varied sizes One tissue Organza or a similar material, cut into pieces, one layered 1” x 1” piece Pillow stuffing, one 1” x 1” piece A tsp of sand Three paper clips Liquid glue Scissors Baby Wipes Preparation Use tape to divide the large cardstock sheet in four sections: one for the cloud title at the top and three for the altitudes: using a portrait layout, place three pieces of tape horizontally, from side to side of the sheet. 1. 1” off the upper edge of the sheet 2. 8” off the upper edge of the sheet 1 Steps What to do and how to do it: Making A Tactile Cloud Identification Chart 1. Discuss that clouds come in three basic shapes: cirrus, stratus and cumulus. a. Feel of the 4” feather and describe it; discuss that these wispy clouds are high in the sky and are named cirrus. -
A Numerical Case Study of Upper Boundary Condition in MM5 Over
A CASE STUDY OF THE IMPACT OF THE UPPER BOUNDARY CONDITION IN POLAR MM5 SIMULATIONS OVER ANTARCTICA Helin Wei1, David H. Bromwich1, 2 , Le-Sheng Bai1, Ying-Hwa Kuo3, and Tae Kwon Wee3 1Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University 2Atmospheric Sciences Program, Department of Geography, The Ohio State University 3MMM Division, National Center for Atmospheric Research, Boulder, Colorado 1. Introduction generally applied numerical models because the 1In limited area modeling, the upper boundary vertical wavenumber and frequency of the radiated condition was not addressed extensively until waves must be specified. Second, it also requires nonhydrostatic models became widely available a relatively deep model domain so that the vertical for numerical weather prediction. Ideally, the radiation of gravity wave energy is of secondary boundary condition should be imposed in such importance at the model top, otherwise the way that makes the flow behave as if the spurious momentum flux is not negligible (Klemp boundaries were not there. and Durran, 1983). In the past, the rigid lid upper boundary Another prominent scheme is the absorbing condition was utilized because it purportedly upper boundary condition. It is designed to damp eliminated rapidly moving external gravity waves out the vertically propogating waves within an from the solutions, thereby permitting longer time upper boundary buffer zone before they reach the dp top boundary by using smoothing, filtering or some steps. This condition requires ω = = 0 at the other approach, such as adding frictional terms to dt model momentum equations. model top. The effectiveness of this approach has Previous studies show that the radiation and been demonstrated when the model top is set far absorbing upper boundary conditions reduce the above from the region of interest or the advective wave reflection to a great extent, however little effects are dominant in comparison to the vertical work has been done to investigate how they propogation of internal gravity wave energy. -
Future Crop Yield Projections Using a Multi-Model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States
atmosphere Article Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States D. W. Shin 1, Steven Cocke 1, Guillermo A. Baigorria 2, Consuelo C. Romero 2, Baek-Min Kim 3 and Ki-Young Kim 4,* 1 Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306-2840, USA; [email protected] (D.W.S.); [email protected] (S.C.) 2 Next Season Systems LLC, Lincoln, NE 68506, USA; [email protected] (G.A.B.); [email protected] (C.C.R.) 3 Department of Environmental Atmospheric Sciences, Pukyung National University, Busan 48513, Korea; [email protected] 4 4D Solution Co. Ltd., Seoul 08511, Korea * Correspondence: [email protected]; Tel.: +82-2-878-0126 Received: 28 October 2020; Accepted: 27 November 2020; Published: 30 November 2020 Abstract: Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 C) and minor rainfall decreases (~ 0.10 mm/day) with pattern shifts ◦ − in the southeast United States. -
Innovative Mini Ultralight Radioprobes to Track Lagrangian Turbulence Fluctuations Within Warm Clouds: Electronic Design
sensors Article Innovative Mini Ultralight Radioprobes to Track Lagrangian Turbulence Fluctuations within Warm Clouds: Electronic Design Miryam E. Paredes Quintanilla 1,* , Shahbozbek Abdunabiev 1, Marco Allegretti 1, Andrea Merlone 2 , Chiara Musacchio 2 , Eros G. A. Pasero 1, Daniela Tordella 3 and Flavio Canavero 1 1 Politecnico di Torino, Department of Electronics and Telecommunications (DET), Corso Duca Degli Abruzzi 24, 10129 Torino, Italy; [email protected] (S.A.); [email protected] (M.A.); [email protected] (E.G.A.P.); fl[email protected] (F.C.) 2 Istituto Nazionale di Ricerca Metrologica, Str. Delle Cacce, 91, 10135 Torino, Italy; [email protected] (A.M.); [email protected] (C.M.) 3 Politecnico di Torino, Department of Applied Science and Technology (DISAT), Corso Duca Degli Abruzzi 24, 10129 Torino, Italy; [email protected] * Correspondence: [email protected] Abstract: Characterization of dynamics inside clouds remains a challenging task for weather forecast- ing and climate modeling as cloud properties depend on interdependent natural processes at micro- and macro-scales. Turbulence plays an important role in particle dynamics inside clouds; however, turbulence mechanisms are not yet fully understood partly due to the difficulty of measuring clouds at the smallest scales. To address these knowledge gaps, an experimental method for measuring the influence of small-scale turbulence in cloud formation in situ and producing an in-field cloud Citation: Paredes Quintanilla, M.E.; Lagrangian dataset is being developed by means of innovative ultralight radioprobes. This paper Abdunabiev, S.; Allegretti, M.; presents the electronic system design along with the obtained results from laboratory and field exper- Merlone, A.; Musacchio, C.; Pasero, ≈ ≈ E.G.A.; Tordella, D.; Canavero, F. -
A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity
atmosphere Article A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity Xia Sun 1 , Lian Xie 1,*, Shahil Umeshkumar Shah 2 and Xipeng Shen 2 1 Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Box 8208, Raleigh, NC 27695-8208, USA; [email protected] 2 Department of Computer Sciences, North Carolina State University, Raleigh, NC 27695-8206, USA; [email protected] (S.U.S.); [email protected] (X.S.) * Correspondence: [email protected] Abstract: In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by Citation: Sun, X.; Xie, L.; Shah, S.U.; individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE Shen, X. A Machine Learning Based predictions often show higher skill score than individual model forecasts and the SAE predictions. Ensemble Forecasting Optimization Algorithm for Preseason Prediction of However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when Atlantic Hurricane Activity. -
Saharan Dust, Convective Lofting, Aerosol Enhancement Zones, And
PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Saharan dust, convective lofting, aerosol enhancement zones, 10.1002/2017JD026933 and potential impacts on ice nucleation in the tropical Key Points: upper troposphere • Relative to background upper tropospheric air, an aerosol C. H. Twohy1 , B. E. Anderson2, R. A. Ferrare2 , K. E. Sauter3 ,T.S.L’Ecuyer3 , enhancement zone (AEZ) exists at the 4 fi 5 2 2 bottom edge of tropical storm anvils S. C. van den Heever , A. J. Heyms eld , S. Ismail , and G. S. Diskin • Storms affected by the SAL have more 1 2 large particles, likely mineral dust, in NorthWest Research Associates, Redmond, Washington, USA, NASA Langley Research Center, Hampton, Virginia, USA, the AEZ and these contribute to the 3Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison, Wisconsin, USA, 4Department of background concentration Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA, 5Microscale and Mesoscale Meteorology • Convective lofting of dust by Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA convective systems is predicted to enhance ice nucleating particle concentrations in the upper fi fl troposphere Abstract Dry aerosol size distributions and scattering coef cients were measured on 10 ights in 32 clear-air regions adjacent to tropical storm anvils over the eastern Atlantic Ocean. Aerosol properties in these regions were compared with those from background air in the upper troposphere at least 40 km from Supporting Information: • Supporting Information S1 clouds. Median values for aerosol scattering coefficient and particle number concentration >0.3 μm diameter were higher at the anvil edges than in background air, showing that convective clouds loft particles from the Correspondence to: lower troposphere to the upper troposphere. -
The Development of the Atmospheric Measurements by Ultra-Light Spectrometer (AMULSE) Greenhouse Gas Profiling System and Applica
Atmos. Meas. Tech., 13, 3099–3118, 2020 https://doi.org/10.5194/amt-13-3099-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. The development of the Atmospheric Measurements by Ultra-Light Spectrometer (AMULSE) greenhouse gas profiling system and application for satellite retrieval validation Lilian Joly1, Olivier Coopmann2, Vincent Guidard2, Thomas Decarpenterie1, Nicolas Dumelié1, Julien Cousin1, Jérémie Burgalat1, Nicolas Chauvin1, Grégory Albora1, Rabih Maamary1, Zineb Miftah El Khair1, Diane Tzanos2, Joël Barrié2, Éric Moulin2, Patrick Aressy2, and Anne Belleudy2 1GSMA, UMR CNRS 7331, U.F.R. Sciences Exactes et Naturelles, Université de Reims, Reims, France 2CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France Correspondence: Lilian Joly ([email protected]) and Olivier Coopmann ([email protected]) Received: 3 September 2019 – Discussion started: 25 September 2019 Revised: 4 April 2020 – Accepted: 22 April 2020 – Published: 12 June 2020 Abstract. We report in this paper the development of an em- files from the assimilation of infrared satellite observations. bedded ultralight spectrometer ( < 3 kg) based on tuneable The results show that the simulations of infrared satellite ob- diode laser absorption spectroscopy (with a sampling rate of servations from IASI (Infrared Atmospheric Sounding Inter- 24 Hz) in the mid-infrared spectral region. This instrument is ferometer) and CrIS (Cross-track Infrared Sounder) instru- dedicated to in situ measurements of the vertical profile con- ments performed in operational mode for numerical weather centrations of three main greenhouse gases – carbon dioxide prediction (NWP) by the radiative transfer model (RTM) RT- (CO2), methane (CH4) and water vapour (H2O) – via stan- TOV (Radiative Transfer for the TIROS Operational Ver- dard weather and tethered balloons.