African Easterly Waves in 30-Day High-Resolution Global Simulations: a Case Study During the 2006 NAMMA Period

African Easterly Waves in 30-day High-resolution Global Simulations: A Case Study during the 2006 NAMMA Period

Bo-Wen Shen1,2, Wei-Kuo Tao2, Man-Li C. Wu2 1UMCP/ESSIC; 2NASA/GSFC 5th COAA Taipei, Taiwan, June 28-30, 2010

1 Acknowledgements

NASA/GSFC: Christa Peters-Lidard, Oreste Reale, Kuo-Sen Kuo, Jenny Wu, Phil Webster NASA/HQ: Tsengdar Lee NASA/JPL: Peggy Li Navy Research Lab: Jin Yi NOAA/GFDL: Shian-Jiann Lin UMCP: Antonio Busalacchi NC A&T State U.: Yuh-Lang Lin NASA/ARC: Chris Henze, Piyush Mehrotra, Samson Cheung, Henry Jin, Johnny Chang, . C. Schulbach, R. Ciotti, C. Niggley, S. Chang, W. Thigpen, B. Hood A. Lazanoff, K. Freeman, J. Taft, control-room

Funding sources: NASA ESTO (Earth Science Technology Office) AIST (Advanced Information System Technology) Program; NASA Modeling, Analysis, and Prediction (MAP) Program; NASA Energy and Water Cycle Study (NEWS) Program; National Science Foundation (NSF) Science and Technology Center (STC)

2 Outline

Introduction

Global Mesoscale Modeling with NASA Supercomputing Technology

Simulations of High-impact Tropical Weather • Five AEWs and genesis of Hurricane Helene (2006) in 30-day runs

Summary and Conclusions

3 Scenarios in Decadal Survey

• Extreme Event Warnings (near-term goal): Discovering predictive relationships between meteorological and climatologically events and less obvious precursor conditions from massive data set  multiscale interactions; modulations and feedbacks between large/long-term scale and small/short-term scale flows

• Climate Prediction (long-term goal): Robust estimates of primary climate forcings for improved climate forecasts, including local predictions of the effects of climate change. Data fusion will enhance exploitation of the complementary Earth Science data products to improve climate model predictions.

Courtesy of the Advanced Data Processing Group, ESTO PI WorkShop, Cocoa Beach, FL

4 High-impact Tropical Weather: Hurricanes

Each year tropical cyclones (TCs) cause tremendous economic losses and many fatalities throughout the world. Examples include Hurricane Katrina (2005), which is the costliest Atlantic hurricane in history, and TC Nargis, which is one of the 10 deadliest TCs of all time.

Hurricane Katrina (2005) •Cat 5, 902 hPa, with two stages of rapid intensification •The sixth-strongest Atlantic hurricane ever recorded. •The third-strongest landfalling U.S. hurricane ever recorded. •The costliest Atlantic hurricane in history! ($75 billion) • http://en.wikipedia.org/wiki/Hurricane_Katrina Severe Tropical Storm Nargis (2008) •Deadliest named cyclone in the North Indian Ocean Basin •Short lifecycle: 04/27-05/03, 2008; identified as TC01B at 04/27/12Z by the Joint Typhoon Warning Center (JTWC). •Very intense, with a Minimum Sea Level Pressure of 962 hPa and peak winds of 135 mph (~Category 4) •High Impact: damage ~ $10 billion; fatalities ~ 134,000 •Affected areas: Myanmar (Burma), Bangladesh, India, Srilanka

5 Progress of Hurricane Forecasts (by National Hurricane Center)

Track Errors Intensity Errors

better

Figure: The progress of hurricane forecasts by National Hurricane Center. Horizontal axis indicates year, and vertical axis shows forecast errors. Lines with different color show different forecast intervals. During the past twenty years, track forecasts have been steadily improving (left panel), but Intensity forecasts have lagged behind (right panel).

6 Multiscale Interactions of TC Formation

Global mesoscale Model scale mesoscal/ Cloud Global regional dx~O(1km) (GCMs) dx~O(10km) dx~O(100km) (convection process)

Super-parameterization (multi-scale modeling framework, MMF)

physical Equatorial vortex merger/ MJO CISK/WISHE processes Rossby Wave axisymmetrization

scale interaction modulation vortex dynamics feedback (initial conditions, initialization) (cps, surface/boundary layer)

CISK: conditional instability of second kind; CPs: cumulus parameterizations; MMF: multiscale modeling framework; MJO: Madden-Julian Oscillation; TC: Tropical Cyclone; WISHE: Wind induced surface heat exchange;

7 Challenges

• Satellite Data Challenges: o) Massive data storage o) Display/visualizations (~TB) • Modeling Challenges: o) Explicit representation of (the effects of) convective-scale motions o) Verification of model simulations at high spatial and temporal resolutions o) Understanding and representation of multiscale interactions • Computational Challenges: o) Real-time requirements with supercomputing o) Efficient data I/O (at runtime) and data access (via massive storage systems) o) Parallel computing and parallel I/O with new processors (e.g., multi-cores) o) Processing and visualizations of massive data volumes with large-scale multiple-panel display system

Satellite, Numerical Models, and Supercomputing Technology

8 NASA Major Supercomputers

Columbia Supercomputer (ranked 2nd in late 2004)

• Based on SGI® NUMAflex™ architecture 20 SGI® Altix™ 3700 superclusters, each with 512 processors Global shared memory across 512 processors • 10,240 Intel Itanium® 2 CPUs; Current processor speed: 1.5 gigahertz; Current cache: 6 megabytes • 20 terabytes total memory; 1 terabyte of memory per 512 processors

Pleiades Supercomputer (ranked 3rd in late 2008; 6th in 2010)

• quad-core Xeon 5472 (Harpertown) CPUs, speed - 3GHz; Cache - 12MB per CPU • 81,920 cores in total (512 cores per cabinet) • 120+ TB memory in total, 1 (8) GB memory per core (node) • 3.1 PB disk spaces • InfiniBand, 9,216 compute nodes • 973.4 Tflops/s peak; 722.7 Tflops/s Linpack

Biswas, R., M.J. Aftosmis, C. Kiris, and B.-W. Shen, 2007: Petascale Computing: Impact on Future NASA Missions. Petascale Computing: Architectures and Algorithms, 29-46 (D. Bader, ed.), Chapman and Hall / CRC Press, Boca Raton, FL. 9 Concurrent Visualization System V2.0

Figure : The newly developed Concurrent Visualization (CV) System (Version 2, top panel). Rounded rectangles indicate systems, and rectangles indicate processes. The whole system (from left to right panels) consists of a computing node ("Columbia Node"), a middle-layer system ("coalescer"), and the hyperwall-2 128-node 8-core/node rendering cluster. These systems are used for data extraction, handling, and visualization; and for MPEG image production and visualization display. The first generation CV system (Version 1) is shown in the right panel for comparison. Shen, B.-W., W.-K. Tao, G. Bryan, C. Henze, P. Mehrotra, J.-L. F. Li, S. Cheung, 2009: High-impact Tropical Weather Prediction with the NASA CAMVis: Coupled Advanced multi-scale Modeling and concurrent Visualization Systems. Supercomputing conference 2009, Portland, Oregon, November 14-20, 2009. Green, B., C. Henze, B.-W. Shen, 2010: Development of a scalable concurrent visualization approach for high temporal- and spatial-resolution models. AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010.

10 A Science-Driven Approach

Goals: •to explore the power of supercomputing technology (e.g., supercomputers and visualization systems) on the advancement Large-scale of global weather and hurricane modeling; Scientific Numerical •to discover how hurricanes form, intensify, Discoveries and move with advanced numerical models; Modeling •to understand the underlining mechanisms (how realistic the model depiction of TC dynamics) •to extend the lead-time of hurricane predictions (and high-impact tropical weather predictions): from short-term (~5days) to extended-range (15~30 days) forecasts Supercomputing

In short, to advance numerical models and thus to extend the lead time of hurricane predictions with modern supercomputing technology

11 The Global Mesoscale Model

1. Model Dynamics and Physics:

• The finite-volume dynamical core (Lin 2004); • The NCAR physical parameterizations, and NCEP SAS as an alternative cumulus parameterization scheme • The NCAR land surface model (CLM2, Dai et al. 2003)

2. Computational design, scalability and performance (suitable for running on clusters or multi-core systems)

12 Modeling Approach

• Given an initial mesoscale vortex (TC), we verify model’s performance in predicting its movement and intensification; • Given a large-scale flow (e.g., MJO and/or AEWs), we investigate if modulations of TC activities (e.g., formation) can be simulated realistically; • By performing extended-range (15-30day) simulations, we test if our modeling approach could extend the predictions of an MJO or AEWs. • To understand if and how the lead time of “mesoscale” hurricane prediction can be extended by a global mesoscale model

13 African Easterly Waves (AEWs)

• During the summer time (from June to early October), African easterly waves (AEWs) appear as one of the dominant synoptic weather systems in West Africa. • These waves are characterized by an average westward-propagating speed of 11.6 m/s, an average wavelength of 2200km, and a period of about 2 to 5 days. • It has been documented that some AEWs could develop into hurricanes in the Atlantic and even East Pacific regions (e.g., Carlson, 1969). These hurricanes, which are usually intense, are called "Cape Verde" storms. • In addition, studies also suggested that AEWs could modulate the features of the Inter-Tropical Discontinuity (ITD) over the African continent (e.g., Berry and Thorncroft, 2005 and references therein), where the African northeasterly trade winds and southwesterly monsoon flows meet. • Therefore, improving our understanding and predictions of the West African rainfall and hurricane formation in the Atlantic would rely on the accurate simulations of the AEWs.

14 Map of North Africa

Berry and Thorncroft ( 2005, MWR)

o 10 W 0o 10oE 20oE 30oE 40oE

20oN

10oN

15 Multiscale Interaction during Hurricane Formation

African Easterly Jet (AEJ) / opic rotr Ba nic ocli bar ity abil inst African Easterly

a Wave (AEW) Surface mechanic uine s G land and thermodynamic effects High Hurricane (?)

Horizontal temperature gradient

16 Five AEWs in 30-day Simulations

(init at 00zz Aug 22, 2006)

V winds averaged over 5o-20oN 30-day averaged U winds (10oE) 30-day averaged U winds (20oE)

22 Aug TEJ NCEP Reanalysis

AEJ pressure pressure

21 Sep

22 Aug High-res TEJ GCM pressure pressure AEJ H

21 Sep 50oW Longitude 40oE 30oN Latitude EQ 30oN Latitude EQ

Shen, B.-W. et al., 2010c: African Easterly Waves and African Easterly Jet in 30-day High- resolution Global Simulations. A Case Study during the 2006 NAMMA period. Submitted.

17 30-day Averaged U Winds and Temp

(init at 08/22/00z) NCEP Reanalysis Model Simulations

Shen, B.-W. et al., 2010c: African Easterly Waves and African Easterly Jet in 30-day High- resolution Global Simulations. A Case Study during the 2006 NAMMA period. Submitted.

18 30-day Averaged Precip and 850 hPa Winds (init at 08/22/00z)

TRMM Precip and NCEP Aeanalysis Winds

Model Simulations

Shen, B.-W. et al., 2010c: African Easterly Waves and African Easterly Jet in 30-day High- resolution Global Simulations. A Case Study during the 2006 NAMMA period. Submitted.

19 Verification with NAMMA Observations

Praia, CV @ (23.5oW, 14.9oN)

NAMMA OBS NCEP Reanalysis Model simulation

1 1 5 5 2 4 2 4 3 1 5 3 2 3 4

Left panel: Schmidlin, F. J., B. Morrison, T. Baldwin, E. T. Northam, 2007: High Resolution Radiosonde Measurements from Cape Verde: Details of Easterly Wave Passage. AGU 2007 Fall Meeting.

NAMMA: NASA African Monsoon Multidisciplinary Analyses

Shen, B.-W. et al., 2010c: African Easterly Waves and African Easterly Jet in 30-day High- resolution Global Simulations. A Case Study during the 2006 NAMMA period. Submitted.

20 Simulations of Helene (2006) between Day 22-30

Helene: 12-24 September, 2006

Track Forecast Intensity Forecast

model

OBS

model

Future work: to study multiscale interactions among TEJ, AEJ, AEWs, hurricanes and surface mechanic and thermodynamic processes

Shen, B.-W. et al., 2010c: African Easterly Waves and African Easterly Jet in 30-day High- resolution Global Simulations. A Case Study during the 2006 NAMMA period. Submitted.

21 Sensitivity Experiments

Case id Dynamic Clm and SST Guinea Highlands Remarks IC Physics IC cntl 08/22 08/22 weekly

A 08/23 08/23 weekly

B 08/24 08/24 weekly

C 08/25 08/25 weekly

D 08/22 Climate clm weekly

E 08/22 02/22 weekly

F 08/22 08/22 climate

G 04/22 04/22 weekly Changed date to be 08/22/2006 H 06/22 06/22 weekly Changed date to be 08/22/2006 I 08/22 08/22 weekly A factor of 0.6 in heights

Shen, B.-W. et al., 2010c: African Easterly Waves and African Easterly Jet in 30-days High- resolution Global Simulations. A Case Study during the 2006 NAMMA period. Submitted.

22 Sensitivity Experiments

23 Predictability at Different Scales

Global mesoscale

mesoscale cloud scale synoptic scale (convection process)

Super-parameterization (multi-scale modeling framework, MMF)

• Predictability at different scales may not be totally independent. • With regional models, researchers previously focused on improving convective-scale flows and their upscaling/feedback (timing and location) processes, and thus mesoscale flows. • Here we try to point out the importance of improving two-way interactions between these synoptic- and meso-scale using a global mesoscale model, in particular, if the above approach may have a “theoretical limit” on the improvement of the mesoscale predictability. • In addition, the aforementioned multiscale interaciotns with resolved convection seem to be more realistic, after the uncertainties of CPs are removed.

24 Summary

Improved forecasts of TC track, intensity and NASA Global Mesoscale Model: one of the formation first ultra-high resolution GCMs Improved extended-range (15~30 –days) NASA Multi-scale Model Framework: simulations of MJOs and AEWs. consisting of the NASA global model and tens of thousands of copies of Large-scale NASA cloud resolving model (GCE) A unified view on TC formation, including Scientific Numerical Approaches with explicitly-resolved modulation by large-scale flows and Discoveries Modeling convection and/or its effects to reduce interaction between mesoscale the uncertainties of cumulus vortices, surface fluxes and “The Cycle” parameterizations convection.  hierarchical Model Validations with mesoscale weather multiscale interactions systems such as the Catalina Eddy, Future work: extending the current approach Hawaiian Wake, Mei-Yu front etc to study hurricane climate and impact of global change on hurricane climate. Supercomputing

Columbia: SGI Altix, 14,336 cores (Itanium II) Pleiades: SGI Altix ICE, 81,920 cores (Xeon) Hyperwall-2: 128 panels

25 1. ICS, covering the lifecycle of the first AEW 2. Climate CLM- IVP for the 1st AEW, BVP for the rest of AEWs? 3. Clm in April, - support (2) 4. Climate SST  timing to respond, via TCs or large-scale flows 5. Dynamic ICs alone  dependence of AEW initiation on Ics 6. Lower mountain  impact of resolved terrains on the large-scale simulations

26 Computational performance

• 10 days/70 mins with 240 cpus on columbia • 90 days/75 mins with 720 cpus on pleiades

• 5 days/3 hours with 60 cores,  suitable for running on clusters and/or multi-core system

27 Publications (03/15/2009-06/15/2010)

Major publications: • 28. Shen, B.-W. et al., 2009: African Easterly Waves and African Easterly Jet in 30-days High-resolution Global Simulations. A Case Study during the 2006 NAMMA period in 2006. (Submitted.) • 27. Shen, B.-W., W.-K. Tao, W. K. Lau, R. Atlas, 2009: Improving Tropical Cyclogenesis Prediction with a Global Mesoscale Model: Hierarchical Multiscale Interactions During the Formation of Tropical Cyclone Nargis (2008). (submitted to JGR- Atmosphere; in press). • 26. Shen, B.-W., W.-K. Tao, R. Atlas, Y.-L. Lin, C.-D. Peters-Lidard, J.-D. Chern, K.-S. Kuo, 2009: Forecasting Tropical Cyclogenesis with a Global Mesoscale Model: Preliminary Results for Twin Tropical Cyclones in May 2002. (Submitted). • 25. Shen, B.-W., W.-K. Tao, G. Bryan, C. Henze, P. Mehrotra, J.-L. F. Li, S. Cheung, 2009: High-impact Tropical Weather Prediction with the NASA CAMVis: Coupled Advanced multi-scale Modeling and concurrent Visualization Systems. Supercomputing conference 2009, Portland, Oregon, November 14-20, 2009. • 24. W.-K. Tao, D. Anderson, J. Chern, J. Entin, A. Hou, P. Houser, R. Kakar, S. Lang, W. Lau, C. Peters-Lidard, X. Li, T. Matsui, M. Rienecker, M. R. Schoeberl, B.-W. Shen, J. J. Shi, and X. Zeng, 2008: A Goddard Multi-Scale Modeling System with Unified Physics. Submitted to Annales Geophysicae Special Issue dedicated to The 1st International Conference on From Desert to Monsoons (in press).

Conference Presentations/Reprint/Seminars: • 23. Shen, B.-W., W.-K. Tao, W. K. Lau, J. Chern, J.-L. F. Li, B. Green, R. Atlas, 2010: Global Multiscale Modeling with NASA Supercomputing Technology: Mutiscale Simulations of Tropical Cyclogenesis associated with an MJO or AEW. AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010. (submitted) • 22. Li, J.-L. F, D. E. Waliser, J. Chern, B.-W. Shen, W.-T. A. Chen, J. Teixeira, W.-K. Tao, T. L. Kubar, 2010: Cloud Vertical Structure in the simulations with Conventional GCMs and a Multi-scale Modeling Framework GCM, Global Analyses and CloudSat observations. AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010. (submitted) • 21. Green, B., C. Henze, B.-W. Shen, 2010: Development of a scalable concurrent visualization approach for high temporal- and spatial-resolution models. AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010. (submitted)

28 Publications (03/15/2009-06/15/2010)

• 20. Chern, J.-D., W.-K. Tao, B.-W. Shen, T. Matsui, J.-L. F Li, D. E Waliser, 2010: Evaluations of the Global and Regional Energy and Water Cycles in a Multiscale Modeling Framework System. AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010. (submitted) • 19. Shen, B.-W., W.-K. Tao, W. K. Lau, R. Atlas, 2009: Predicting Tropical Cyclogenesis with a Global Mesoscale Model: Hierarchical Multiscale Interactions During the Formation of Tropical Cyclone Nargis (2008). AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010. (submitted) • 18. Shen, B.-W., W.-K. Tao, G. Bryan, C. Henze, P. Mehrotra, J.-L. F. Li, 2009: High-impact Tropical Weather Prediction with the NASA Coupled Advanced multi-scale Modeling and concurrent Visualization Systems (CAMVis). The NASA ESTO AIST 2010 Workshop: Decadal Survey Era Capabilities and Technology Roadmap, Cocoa Beach, FL, February 8-11 2010. • 17. Shen, B.-W., S.-J. Lin, J.-L. Lee, M. Satoh, and R. Atlas, 2010: "High-resolution Global and Regional Modeling and Simulations of High-impact Weather and Climate", AGU 2010 Western Pacific Geophysics Meeting, Taipei, Taiwan, June 22-25, 2010. (session proposal; accepted) • 16 Shen, B.-W. and K.-S. Kuo, 2009: Twin Tropical Cyclones associated with Madden-Julian Oscillation in the Indian Ocean, Journal of Earth Science Phenomena, 2009, 13. • 15. Shen, B.-W., 2009: Global Mesoscale Modeling with NASA Supercomputing Technology: Hierarchical Multi-scale Interactions during the Formation of Tropical Cyclone Nargis (2008). National Central University, Chung-Li, Taiwan, January 5, 2010. (invited talk) • 14. Shen, B.-W., 2009: Application of a NASA Global Mesoscale Model to Study Tropical Cyclogenesis associated with an MJO or AEW. National Taiwan Normal University, Taipei, Taiwan December 22, 2009. (invited talk) • 13. Shen, B.-W., W.-K. Tao, W. K. Lau and R. Atlas, 2009: Global Multiscale Modeling on NASA Supercomputers: Extended- Range Simulations of Madden-Julian Oscillations and African Easterly Waves. Fifth International Ocean-Atmosphere Conference (COAA2010), Taipei, Taiwan, June 28-30, 2010. (an abstract submitted) • 12. Shen, B.-W. and W.-K. Tao, 2009: Application of a Global Mesoscale Model to Study Hierarchical Multiscale Interactions during the Tropical Cyclogenesis associated with African Easterly Waves in 2006. AGU 2009 Fall Meeting, San Francisco, December 14-18, 2009. (poster presentation) • 11. Shen, B.-W., 2009: Extending the Lead Time of Tropical Cyclogenesis Prediction with a Global Mesoscale Model. Supercomputing Conference 2009, Portland, Oregon, November 14-20, 2009. (selected as one of top NASA project demonstrations at SC09). (booth demonstration; oral presentations)

29 Publications (03/15/2009-06/15/2010)

• 10. Shen, B.-W., 2009: Global Mesoscale Modeling with NASA Supercomputing Technology: Hierarchical Multi-scale Interactions during Tropical Cyclogenesis associated with an MJO or AEW. M Square Office Building of UMCP/ESSIC, November 9, 2009. (an invited talk) • 9. Shen, B.- W., 2009c: In Support of Hurricane Forecasting Research at CWB in Taiwan. October, 7, 2009 (in Chinese). (an invited news article) • 8. Shen, B.- W., 2009b: Supercomputer and Typhoon Prediction. Want daily forum. September 8, 2009. (in Chinese). (an invited news article) • 7. Shen, B.-W., 2009a: Current Challenges of Hurricane Intensity Predictions in Taiwan. Want daily forum and China Times. August 14, 2009. (in Chinese). • 6. Shen, B.-W., 2009: Global Mesoscale Modeling with NASA Supercomputing Technology: Hierarchical Multi-scale Interactions during Tropical Cyclogenesis associated with an MJO or AEW. The Atlantic Oceanographic and Meteorological Laboratory (AOML) / Hurricane Research Division (HRD) , Miami, FL, August 17-21, 2009. (an invited talk). • 5. Shen, B.-W. and W.-K. Tao, 2009: Global Multiscale Modeling on NASA Supercomputers: Extended-Range Simulations of MJOs and AEWs. 7th CMMAP Bi-annual team meeting, Fort Collins, CO, July 27-30, 2009. (poster presentation) • 4. Shen, B.-W. and W.-K. Tao, W. K. Lau, R. Atlas, and J. Chern, 2009: Hierarchical Multi-scale Interactions during Tropical Cyclone Formation associated with an MJO or AEW. 7th CMMAP Bi-annual team meeting, Fort Collins, CO, July 27-30, 2009. (oral presentation) • 3. Chern, J., W.-K. Tao, T. Matsui, and B.-W. Shen, 2009: Evaluating Goddard Multi-scale MOdeling Framework Through the TRMM Triple-Sensor Three-Step Evaluation Framework (T3EF). 7th CMMAP Bi-annual team meeting, Fort Collins, CO, July 27- 30, 2009. (poster presentation) • 2. Shen, B.-W. and W.-K. Tao, 2009: Global Multiscale Modeling on NASA Supercomputers: Extended-Range Simulations of MJOs and AEWs. The EarthCARE Workshop 2009, Kyoto, Japan, June 10-12, 2009. (session chair; poster presentation) • 1. Shen, B.-W., 2009: Global Multi-scale Modeling on NASA Supercomputers: Application to Hurricane Forecasts. The first project meeting of AIST Project CAMVis. NASA/ARC/NAS, Moffett Field, CA, May 11, 2009. (oral presentation)

30 Acronym

• 3D-Winds 3D Tropospheric Winds from Space-based Lidar (DS mission) • ACE Aerosol-Cloud-Ecosystem (DS mission) • CAMVis Coupled NASA Advanced Multiscale Model and Concurrent Visualization Systems • CISK: Conditional Instability of second kind • Columbia: supercomputer at NASA/ARC, operating in late 2004 • CPs: Cumulus (or Cloud) parameterizations • CVs: concurrent visualization (CV) system • fvGCM: finite-volume General Circulation Model (global model) • GCE: Goddard Ensemble Model (cloud model) • GCM: General Circulation Model • HW-2: hyperwall-2 • Ifort: Intel Fortran Compiler (version 10, 11) • IPoIB: Internet Protocol over InifiBand • MJO Madden-Julian Oscillation (large-scale tropical weahter) • MMF (fvMMF) fvGCM + tens of thousands of GCEs • MPI: message passing interface, widely used in distributed-memory system • NHC: National Hurricane Center • OBS: observation • OMP: OpenMP, multi-threaded programming paradigm, suitable for shared-memory machines • PATH Precipitation and All-weather Temperature and Humidity (DS mission) • Pleiades: supercomputer at NASA/ARC, operating in late 2008 • Procs: processes • RDMA: remote direct memory access • SMAP Soil Moisture Active-Passive (DS mission) • TC: tropical cyclone, a generic term for hurricanes, typhoons, cyclones • vis: visualization • WCRP: World Climate Research Programme • WISHE: wind induced surface heat exchange • WWRP/THORPEX: World Weather Research Programme/ \The Observing System Research and Predictability Experiment • XOVWM Extended Ocean Vector Winds Mission (DS mission) • YOTC: Year of Tropical Convection

31 Backup Slides

32 Visualizations of Scale Interactions

(a) 12 h (b) 48 h Before Ivan made landfall, Blue: low-level winds scale interaction between its L Red: upper-level winds outflow and an upper-level L A 5-days Forecast of trough (labeled as “L”) might Hurricane Ivan (2004), have been contributed to starting at 1200 UTC Ivan’s intensification, as September 11 indicated by purple lines in panel (d).

L (c) 76 h (d) 120 h Enhancement of upper-level winds, as shown by pink color L lines

IVAN

33 Education and Public Outreach

• Project demonstration on magic planet at Maryland day, April 25, 2009.

• Bo-Wen Shen was interviewed by Earth Magazine of American Geological Institute to provide comments on the recent article entitled “Maximum hurricane intensity preceded by increase in lightning frequency” by Price et al. (2009), which was published by Nature Geoscience. As the current progress of hurricane intensity prediction is slow, and accurate intensity prediction with a numerical model is still very challenging, Shen agrees that correlation between hurricane intensity and lightning frequency could be a useful indicator to predict hurricane intensity.

• Bo-Wen Shen (UMCP/ESSIC, 613.1) published three news articles (in Chinese) to discuss the challenges of intensity and rainfall predictions for landfalling Typhoons in Taiwan, including deadly Typhoon Morakot, and the recent advance in high-resolution global models and supercomputer technology at NASA that show a potential for improving intensity predictions. Morakot (2009) devastated Taiwan and Eastern China, and caused tremendous damage by dumping a total of about 2.5 meters (100 inches) of rain in Taiwan in early August, 2009.

34 Visualizations on the Magic Planet at Maryland Day (April 25, 2009)

35 WCRP (World Climate Research Progreamme)

• The World Climate Research Programme, sponsored by the International Council for Science (ICSU), the World Meteorological Organization (WMO) and the Intergovernmental Oceanographic Commission (IOC) of UNESCO, is uniquely positioned to draw on the totality of climate-related systems, facilities and intellectual capabilities of more than 185 countries. Integrating new observations, research facilities and scientific breakthroughs is essential to progress in the inherently global task of advancing understanding of the processes that determine our climate. • The two overarching objectives of the WCRP are: to determine the predictability of climate; and to determine the effect of human activities on climate • To achieve its objectives, the WCRP adopts a multi-disciplinary approach, organizes large-scale observational and modelling projects and facilitates focus on aspects of climate too large and complex to be addressed by any one nation or single scientific discipline. • Scientific guidance for the WCRP is provided by the Joint Scientific Committee (JSC), consisting of 18 scientists selected by mutual agreement between the three sponsoring organizations and representing climate-related disciplines in atmospheric, oceanic, hydrological and cryospheric sciences. JSC for WCRP (as from 1 January 2010): Chair Professor A. Busalacchi ESSIC, University of Maryland, US

36 WWRP/THORPEX

• WWRP: World Weather Research Programme • THORPEX: The Observing System Research and Predictability Experiment • THORPEX is a 10-year international research and development programme to accelerate improvements in the accuracy of one-day to two-week high impact weather forecasts for the benefit of society, the economy and the environment. • THORPEX establishes an organizational framework that addresses weather research and forecast problems whose solutions will be accelerated through international collaboration among academic institutions, operational forecast centres and users of forecast products • WMO AREP WWRP • The Atmospheric Research and Environment Programme (AREP) co-ordinates and stimulates research on the composition of the atmosphere and weather forecasting, focusing on extreme weather events and socio-economic impacts. AREP supports global research initiatives under programmatic guidance from the Commission for Atmospheric Sciences (CAS). It does this through two programmes: Global Atmosphere Watch (GAW) and the World Weather Research Programme (WWRP) including THORPEX.

37 Year of Tropical Convection

• The Year of Tropical Convection (YOTC), which commenced on 1 August 2008, is a joint World Climate Research Programme (WCRP) – World Weather Research Programme (WWRP)/THORPEX initiative. YOTC is a year of coordinated observing, modelling, and forecasting with a focus on organized tropical convection, its prediction, and predictability. The realistic representation of tropical convection in global models is a long-standing, grand challenge for both numerical weather prediction and climate prediction. Incomplete knowledge and practical issues in this area disadvantage the modelling and prediction of prominent phenomena of the tropical atmosphere across a wide range of scales. Scientific challenges encompass the prediction of El Niño-Southern Oscillation (ENSO), monsoons’ active/break periods, tropical cyclones and many other tropical (and extratropical) features. During YOTC the intent is to exploit the vast amounts of existing and emerging observations, the expanding computational resources and the development of new, high-resolution modelling frameworks. This focussed activity and its ultimate success, will benefit from the coordination of a wide range of ongoing and planned international programmatic activities and collaboration among the operational prediction, research laboratory and academic communities.

38 Tropical Cyclones and Climate Change Knutson et al., 2010, NGEO779

• For future projections of tropical cyclone activity, the challenge is to develop both a reliable projection of changes in the various factors influencing TCs, both local and remote, and a means of simulating the effect of these climate changes, such as storm frequency, intensity and track distribution.  environmental factors and their impact on TC activities,

• Uncertainties in model projections of future TC activitiy arise owing to both uncertainties in how the large-scale tropical climate will change and uncertainties in the implications of these changes for TC activity.

• Although each of global climate models project a substantial increase in tropical SSTs during the twenty-first century, important differences exist in the regional-scale details of their projects, which lead to marked differences in the downscaled regional projections of TC activity.

• We cannot at this time conclusively identify anthropogenic signals in past TC area. • - obervational, theoretical and modeling studies, ….. but still have many limitations.

39 • For all cyclone parameters, projected changes for individual basins show large variations between different modeling studies. - “predictability” at regional scales is not very good,

• “we have very low confidence in projected changes in individual basins”  physical parameterizations are not universal???? • “we have low confidence in projected changes on TC genesis-location, tracks, duration and areas of impacts. Existing model projection do no show dramatic large-scale changes in these features.  large-scale flows are ok, but their modulations on TC activities are different???

• High-resolution dynamical models consistently indicate that greehouse warming will cause the globally averaged intensity of TCs to shift towards stronger storms (with intensity increase of 2-11% by 2100), … decreases in the globally averaged frequency of TCs, by 6-34%. • Rainfall rates are likely to increase

40 The need of 3D Visualizations for the representation of multiscale interactions

High-resolution simulations with this model show that the formation of TC Nargis can be realistically predicted up to 5 days in advance. It is suggested the improved representation of the environmental conditions (listed in the figure) and their hierarchical multiscale interactions were the key to achieving this lead time. Though these multiple processes and their scale interactions can be realistically simulated with the advanced global mesoscale model, presentation of these processes in a simple form for education and outreach purposes becomes very challenging. To achieve this goal, 3D visualizations will be illustrated in the next slide.

Shen, B.-W., W.-K. Tao, W. K. Lau, R. Atlas, 2009: Improving Tropical Cyclogenesis Prediction with a Global Mesoscale Model: Hierarchical Multiscale Interactions During the Formation of Tropical Cyclone Nargis (2008). (submitted to JGR, in press).

41 Coupling NASA Advanced Multi-Scale Modeling and Concurrent Visualization Systems for Improving Predictions of Tropical High-Impact Weather (CAMVis) PI: Bo-Wen Shen (UMD/ESSIC)

Objective (a) (b) CAMVis weather prediction tool will be developed to achieve the following goals by seamlessly integrating NASA technologies (including advanced multiscale modeling visualizations and supercomputing): • to inter-compare satellite “observations” and model simulations at fine resolution, aimed at improving understanding of consistency of satellite-derived fields; (c) (d) •to improve the insightful understanding of the roles of atmospheric moist thermodynamic processes and cloud- radiation-aerosol interactions with 3D visualizations; •to improve real-time prediction of high-impact tropical weather at different scales. Project CAMVis has the potential for supporting the (a,) fvGCM, showing global simulations of tropical cyclone Nargis (2008) and westerly wind belt in the Indian Ocean, (b) mgGCE with multiple copies of GCEs, simulating cloud following NRC Decadal Survey Earth Science missions, motions, (c) Concurrent visualization (CV) system on the 128-panel , and (d) Pleiades including CLARREO, ACE, PATH, 3D-Winds. supercomputer with 51,200 cores

Approach Key Milestones 1. Improve parallel scalability of the multi-scale modeling system to take full advantage of the next-generation peta- • Implement (update) model components and CV on the scale supercomputers (e.g., NASA Pleiades) Pleiades (Columbia) supercomputer; 2. Integrate NASA multiscale model system, including • Conduct initial benchmarks 09/2009 Goddard Cloud Ensemble model (GCE) and the finite-volume • Improve parallel scalability of model components; General Circulation Model (fvGCM), and the concurrent • Integrate the NASA fvGCM and CV; Develop the super- visualization (CV) system component mgGCE 03/2010 3. Significantly streamline data flow for fast processing and • Couple the NASA mgGCE and CV; visualizations ; Conduct high-resolution numerical runs • Implement and test an I/O module 09/2010 4. Test coupled systems • Integrate the fvMMF (fvGCM+mgGCE) and CV 03/2011 Co-I’s/Partners • Streamline data flow for production runs 09/2011 • Test the CAMVis system; Produce results 03/2012 • Co-I’s: Wei-Kuo Tao (GSFC, Co-PI), Bryan Green (Co-PI), Chris Henze, Piyush Mehrotra, (ARC), Jui-Lin Li (JPL) TRL = 3, TRL = 4 • Partners: Antonio Busalacchi (UMD), Peggy Li (JPL) in current 42

Hierarchical Multiscale Interactions

Meaning: • multiscale but “asymmetric” , e.g., large scale vs. small scale (e.g., organization vs. individuals) • dependence of predictability at different scales (“P” may not be totally independent) • (potential) existence of control-feedback-response relationship (at different stages) that can be simulated with numerical models (e.g., Q-E for CPs?) • a different range of “involved” scales at a different stage of a (TC) lifecycle Implications: • From a modeling perspective, clarifications of the following terms might be helpful to understand the hierarchical (two-way) multiscale interactions. 1. lifetime of the large-scale flow (forcing duration): the period that the large-scale flow could act as a forcing, (e.g., barotropic or baroclinic instatilbity); 2. lifetime of the small scale flow; 3. response time: the time for small scale flow to respond and adjust (e.g., time for the initiation of convection); 4. feedback duration: the period that small scale flow can provide feedbacks to large-scale flows (which should be less than its lifetime) how quickly a small-scale flow can respond in reality and in “models”.

• For two-way scale interactions, accurate representation of “forcing” and its “control”, and simulations of “response” and its “feedback” are equally important! how long it will take for small-scale flows to have significant impact on the large-scale flow

43 Scale Interactions

1. Is the initiation of small-scale weather system an IVP? 2. Is the initiation of any consecutive small-scale system an IVP? Does the initiation of the new system depend on the previous small-scale system or the large-scale (environmental) system? 3. If depending on the large-scale system, to what extent the previous small-scale system could impact/change the large-scale system? Phases (timing) and magnitudes (intensity).  what’s impact scale in timing and spacing (on 2nd, 3rd, 4th small-scale system?) 4. From a modeling perspective, spin-run time and initialization time are equally important.

44 Scale Interactions

Deterministic

Stochastic

1. Lifetime of a small-scale weather system (SWS): initiation, intensification, decaying etc. Q: what’s the feedback over a lifecycle? How about aggregate feedbacks over multiple lifecycles 1. If a SWS is stochastic: is initiation or evolution stochastic? Is its feedback stochastic? What happens if its feedback weaker than other forcing? 2. Timing of initiation depends on (i) physical processes and (ii) model spin-run proceeses 3. GCM vs. mg-GCE (3D, IVP?)  AGCM vs. OGCM (“strong” 2D surface condition, BVP?)

45 Modeling Approach

This work is to illustrate the “potential predictability” of high-impact tropical weather with a global meososcale model, which has shown the potential of simulating more realistic hierarchical multiscale interactions (control/response/feedbacks relationship). To achieve our goals of improving our understanding of and confidence in model’s performance in hurricane short-term and climate simulations, we have done the following:

• To understand if and how the lead time of “mesoscale” hurricane prediction can be extended by a global mesoscale model

Model’s predictive ability != Predictability (of a weather system)

46 Physics Parameterizations

• Moist physics: -Deep convections: Zhang and McFarlane (1995); Pan and Wu (1995, aka NCEP/SAS) -Shallow convection: Hack (1994) -large-scale condensation (Sundqvist 1988) -rain evaporation • Boundary Layer - first order closure scheme - local and non-local transport (Holtslag and Boville 1992) • Surface Exchange

- Bryan et al. (1996)

Pan, H.-L., and W.-S. Wu, 1995: Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC office note, No. 409, 40pp. [Available from NCEP].

47 Scientific Visualizations with the NASA CAMVis

• A central question to be addressed: “Is new science being produced (with high-resolution modeling) or just really cool pictures?”, which was raised by Mahlman and others who have reservations (Science, p1040, 2006 August).

• Computational Science (CS) is defined as an inter- disciplinary filed with the goals of understanding and solving complex problems using high-end computing facilities • The next-generation visualizations should help provide the insights of the complicated physical processes in these complex problems (e.g., hurricane prediction) and thus improve our understanding of these processes at different scales and their interactions.

48 Formation of 6 TCs in four 10-day Simulations

Init at Init at 05/06/00z 05/01/00z

Init at Init at 05/11/00z 05/22/00z

Best tracks indicated by blue lines:

Shen, B.-W., W.-K. Tao, R. Atlas, Y.-L. Lin, C.-D. Peters-Lidard, J.-D. Chern, K.-S. Kuo, 2010b: Forecasting Tropical Cyclogenesis with a Global Mesoscale Model: Preliminary Results for Twin Tropical Cyclones in May 2002. (submitted)

49 15-day Simulations of an MJO in 2002

Time

Semidiurnal (?)

50 Westerly Wind Bursts, Low-level Convergence and a pre-TC mesoscale vortex

in 7-day Simulations

850-hPa U winds along lon 88oE Low-level convergence

850-hPa U winds along lon 89oE 20oN E 04/22 04/26 Two phases of enhanced convection

Formation/intensification of a pre-TC mesoscale vortex is related to W (1) the occurrence of westerly wind burst and (2) peak of low-level convergence.

5oS 04/22 04/29 time

51 Averaged precip and 850-hPa winds

Averaged preccip and 850-hPa winds Averaged NASA TRMM precip and from 04/27 (day 5 ) to 04/29 (day 7) NCEP Analysis winds

• Suggesting that the aggregate effects of moist processes (latent heating release, surface fluxes and water vapor transport) are simulated reasonably well ? • Upscaling interactions associated with moist covection? Shen, B.-W., W.-K. Tao, W. K. Lau, R. Atlas, 2010a: Predicting Tropical Cyclogenesis with a Global Mesoscale Model: Hierarchical Multiscale Interactions During the Formation of Tropical Cyclone Nargis (2008) . J. Geophys. Res., doi:10.1029/2009JD013140.

52 Outline

Introduction

Global Mesoscale Modeling with NASA Supercomputing Technology

Simulations of High-impact Tropical Weather • 5-day track/intensity forecasts of Katrina (2005) and Ivan (2004) • 10-day genesis forecasts of Twin TCs (2002) • Multiscal interactions during the formation of Nargis (2008) • 15/30-day simulations of Madden-Julian Oscillations in 2002/2006 • Five AEWs and genesis of Hurricane Helene (2006) in 30-day runs

Global Mesoscale Model as a New Research Tool

Summary and Conclusions

53 African Easterly Jet (AEJ)

Thorncroft and Blackburn, 1999

• AEJ is a midtropospheric jet located over tropical north Africa during the northern hemisphere summer • AEJ is seen as a prominent feature in the zonal wind, with a maximum of around 12.5 m/s at 600-700 hPa and 15oN. • Its vertical shears are crucial in organizing moist convection and generation of squall lines; • Its horizontal and vertical shears are important for the growth of easterly waves; • Below the AEJ, the easterly wind shear is in thermal wind balance with the surface temperature gradient (BWS) • One may view the AEJ as resulting from the combination of diabatically forced meridional circulations which maintain it and easterly waves which weaken it. As the nature of diabatic forcing (e.g., moist or dry convection) differs between models, simulated AEJ is likely to be different (e.g., different heights and/or strengths, which will subsequently affect the easterly waves - model climate • The rate at which the AEJ is maintained is likely to be particularly sensitive to the boundary layer parameterization • Other factors should be included: radiation; the establishment of the surface temperature and humidity gradients themselves; etc