Microphysics Schemes in EMC s Operational Hurricane Models

Similar documents
14B.1 Evaluating cloud microphysics schemes in nested NMMB forecasts

Make American NWP Second to None

Key Laboratory of Mesoscale Severe Weather, Ministry of Education, School of Atmospheric Sciences, Nanjing University

Chapter 7 Precipitation Processes

Collision and Coalescence 3/3/2010. ATS 351 Lab 7 Precipitation. Droplet Growth by Collision and Coalescence. March 7, 2006

ECMWF Workshop on "Parametrization of clouds and precipitation across model resolutions

Data Assimilation Development for the FV3GFSv2

Microphysics. Improving QPF and much more. Greg Thompson. Research Applications Laboratory Nat l Center for Atmospheric Research

The Purdue Lin Microphysics Scheme in WRF. Russ Schumacher AT 730 Final Project 26 April 2006

Track sensitivity to microphysics and radiation

Cloud parameterization and cloud prediction scheme in Eta numerical weather model

Introduction. Effect of aerosols on precipitation: - challenging problem - no agreement between the results (quantitative and qualitative)

1. describe the two methods by which cloud droplets can grow to produce precipitation (pp );

Climate Modeling Issues at GFDL on the Eve of AR5

Precipitations. Terminal Velocity. Chapter 7: Precipitation Processes. Growth of Cloud Droplet Forms of Precipitations Cloud Seeding

Chapter 8 - Precipitation. Rain Drops, Cloud Droplets, and CCN

Chapter 7: Precipitation Processes. ESS5 Prof. Jin-Yi Yu

Tue 3/29/2016. Representation of clouds and precipitation:

Temp 54 Dew Point 41 Relative Humidity 63%

How not to build a Model: Coupling Cloud Parameterizations Across Scales. Andrew Gettelman, NCAR

ANALYSIS OF THE MPAS CONVECTIVE-PERMITTING PHYSICS SUITE IN THE TROPICS WITH DIFFERENT PARAMETERIZATIONS OF CONVECTION REMARKS AND MOTIVATIONS

Cloudy Radiance Data Assimilation

A hierarchy of one- and two-moment microphysical parameterizations in the COSMO model

Water in the Atmosphere

Future directions for parametrization of cloud and precipitation microphysics

An improvement of the SBU-YLIN microphysics scheme in squall line simulation

Name Class Date. 3. In what part of the water cycle do clouds form? a. precipitation b. evaporation c. condensation d. runoff

In this chapter we explain the processes by which nonprecipitating cloud droplets and ice crystals grow large enough to fall as precipitation

WRF Model Simulated Proxy Datasets Used for GOES-R Research Activities

Advancements in Operations and Research on Hurricane Modeling and Ensemble Prediction System at EMC/NOAA

Weather Research and Forecasting Model. Melissa Goering Glen Sampson ATMO 595E November 18, 2004

Introduction to NCEP's time lagged North American Rapid Refresh Ensemble Forecast System (NARRE-TL)

A FROZEN DROP PRECIPITATION MECHANISM OVER AN OPEN OCEAN AND ITS EFFECT ON RAIN, CLOUD PATTERN, AND HEATING

Transitioning Physics Advancements into the Operational Hurricane WRF Model

HWRF sensitivity to cumulus schemes

Aerosol Effects on Water and Ice Clouds

Chapter The transition from water vapor to liquid water is called. a. condensation b. evaporation c. sublimation d.

CAPS Storm-Scale Ensemble Forecast for the NOAA Hazardous Weather Testbed (HWT) Spring Experiment

A Description and Example Output of the WRF-NMM land surface and radiation packages used at NCEP

AEROSOL-CLOUD INTERACTIONS AND PRECIPITATION IN A GLOBAL SCALE. SAHEL Conference April 2007 CILSS Ouagadougou, Burkina Faso

The Impacts of GPSRO Data Assimilation and Four Ices Microphysics Scheme on Simulation of heavy rainfall Events over Taiwan during June 2012

Pd: Date: Page # Describing Weather -- Lesson 1 Study Guide

Deutscher Wetterdienst

The New Thompson Microphysical Scheme in WRF

CAPS Storm-Scale Ensemble Forecasting (SSEF) System

Weather, Atmosphere and Meteorology

Precipitation Processes METR σ is the surface tension, ρ l is the water density, R v is the Gas constant for water vapor, T is the air

Parametrizing Cloud Cover in Large-scale Models

Role of atmospheric aerosol concentration on deep convective precipitation: Cloud-resolving model simulations

Clouds on Mars Cloud Classification

Trade wind inversion. is a highly stable layer (~2 km high) that caps the moist surface layer (often cloudy) from the dry atmosphere above.

Motivation & Goal. We investigate a way to generate PDFs from a single deterministic run

Introduction to Cloud Microphysics

Summary of riming onset conditions for different crystal habits. Semi-dimension: width / lateral dimension (perpendicular to c-axis)

Precipitation Processes. Precipitation Processes 2/24/11. Two Mechanisms that produce raindrops:

Day Microphysics RGB Nephanalysis in daytime. Meteorological Satellite Center, JMA

Snow Microphysics and the Top-Down Approach to Forecasting Winter Weather Precipitation Type

Precipitation. AT350: Ahrens Chapter 8

Exam 2: Cloud Physics April 16, 2008 Physical Meteorology Questions 1-10 are worth 5 points each. Questions are worth 10 points each.

6.2 Meteorology. A meteorologist is a person who uses scientific principles to explain, understand, observe, or forecast Earth s weather.

Weather. Describing Weather

NATS 1750 Lecture. Wednesday 28 th November Pearson Education, Inc.

24.2 Cloud Formation 2/3/2014. Orographic Lifting. Processes That Lift Air Frontal Wedging. Convergence and Localized Convective Lifting

Weather - is the state of the atmosphere at a specific time & place

Precipitation. GEOG/ENST 2331 Lecture 12 Ahrens: Chapter 7

Climate Dynamics (PCC 587): Feedbacks & Clouds

Nathan Hosannah. Advisor Professor J. E. Gonzalez

Thunderstorm-Scale EnKF Analyses Verified with Dual-Polarization, Dual-Doppler Radar Data

Tue 3/1/2016. Arakawa-Schubert, Grell, Tiedtke, Zhang-McFarlane Explicit convection Review for MT exam

Assimilation of cloud/precipitation data at regional scales

Technical Procedures Bulletin

A Study of Convective Initiation Failure on 22 Oct 2004

2012 AHW Stream 1.5 Retrospective Results

Transition of Research to Operations

Numerical Weather Prediction: Data assimilation. Steven Cavallo

Incorporation of 3D Shortwave Radiative Effects within the Weather Research and Forecasting Model

Simulation of the Microphysical Processes and Effect of Latent Heat on a Heavy Rainfall Event in Beijing

A critical review of the design, execution and evaluation of cloud seeding experiments

Assessment of the Noah LSM with Multi-parameterization Options (Noah-MP) within WRF

EARTH SCIENCE. Prentice Hall Water in the Atmosphere Water in the Atmosphere Water in the Atmosphere.

Kessler warm rain microphysics scheme. Haifan Yan

A brief overview of the scheme is given below, taken from the whole description available in Lopez (2002).

Modeling of cloud microphysics: from simple concepts to sophisticated parameterizations. Part I: warm-rain microphysics

Crux of AGW s Flawed Science (Wrong water-vapor feedback and missing ocean influence)

A New Method for Representing Mixed-phase Particle Fall Speeds in Bulk Microphysics Parameterizations

Tropical Cyclone Initialization with Dynamical and Physical constraints derived from Satellite data

MEA 716 Exercise, BMJ CP Scheme With acknowledgements to B. Rozumalski, M. Baldwin, and J. Kain Optional Review Assignment, distributed Th 2/18/2016

Tropical cyclone simulations and predictions with GFDL s prototype global cloud resolving model

Forecasting Local Weather

What do you think of when someone says weather?

Simulation of an Orographic Precipitation Event during IMPROVE-2. Part II: Sensitivity to the Number of Moments in the Bulk Microphysics Scheme

Three things necessary for weather are Heat, Air, Moisture (HAM) Weather takes place in the Troposphere (The lower part of the atmosphere).

The EMC Mission.. In response to operational requirements:

Remote Sensing of Precipitation

Moisture, Clouds, and Precipitation Earth Science, 13e Chapter 17

Direct assimilation of all-sky microwave radiances at ECMWF

A Revised Version of the Cloud Microphysical Parameterization of COSMO-LME

ESCI Cloud Physics and Precipitation Processes Lesson 9 - Precipitation Dr. DeCaria

Aircraft Icing Icing Physics

Sungsu Park, Chris Bretherton, and Phil Rasch

Transcription:

Microphysics Schemes in EMC s Operational Hurricane Models Brad Ferrier, Weiguo Wang, Eric Aligo 1,2 1 Environment Modeling Center (EMC)/NCEP/NWS 2 I.M. Systems Group, Inc. HFIP Physics Workshop 9 11 August 2011

Complex Production Suite (~2009) ¼ of Production Jigsaw Puzzle 8/10/2011 HFIP Physics Workshop 2

Microphysics Summary Feature Scheme Prognostic variables Condensation algorithm Precip fluxes & storage Precipitation type Mixed-phase conditions Zhao-Moorthi (GFS) Water vapor, cloud condensate (water or ice) Sundqvist et al. (RH c ~95%, partial clouds) Top-down integration of precip, no storage, instantaneous fallout. Rain, freezing rain, snow Liquid or ice (supercooled water & ice do not coexist), simple melting Ferrier et al. (2002) (GFDL, HWRF, NAM) Water vapor, total condensate (cld water, rain, cld ice, snow ) Lin et al., Rutl-Hobbs (target RH ~100%) Precip partitioned between storage in grid box & fall out through bottom of box Rain, freezing rain, snow/graupel/sleet Mixed-phase as cold as -40 C, more complex melting & freezing processes 8/10/2011 HFIP Physics Workshop 3

Advecting Total Condensate ( Ferr ) Water vapor (q v ), total condensate (q t ) advected in model (efficient) Cloud water (q w ), rain (q r ), cloud ice (q i ), precip ice (q s ) in microphysics Arrays store fraction of condensate in form of ice (F i ), fraction of liquid in form of rain (F r ; 0 F i, F r 1), fixed between microphysics calls. q t = q w + q r + q i + q s, q ice = q i + q s F i = q ice /q t, F r = q r /(q w + q r ) Internal to microphysics Rest of model q i + q s q t, F i =1, F r =0 z q w + q i + q s q w + q r + q s q w + q r 0 C q t, 0<F i <1, F r =0 q t, 0<F i <1, 0<F r< 0 q t, F i =0, 0<F r <1 8/10/2011 HFIP Physics Workshop 4

K Height Precipitation Sedimentation (1 of 3) (a) z) N+1 P K-1 - Time-accumulated fall of precipitation into grid box from above at time N+1 (P rate T phy ) q K N - Existing precipitation mixing ratio in the grid box at time N (a) Fall of precipitation into grid from above + already existing precipitation 8/10/2011 HFIP Physics Workshop 5

K Height Precipitation Sedimentation (2 of 3) (a) (b) z) N+1 P K-1 N+1 P K-1 q K N q K N* (a) Fall of precipitation into grid from above + pre-existing precipitation (b) Calculate microphysical sources/sinks based on estimate of time-averaged precipitation mixing ratio, q K N* 8/10/2011 HFIP Physics Workshop 6

K Height Precipitation Sedimentation (3 of 3) (a) (b) (c) z) N+1 P K-1 N+1 P K-1 N+1 P K-1 q K N q K N* q K N+1 (a) Fall of precip into grid from above + pre-existing precip (b) Calculate microphysical sources/sinks based on time-averaged estimate of mixing ratio, q K N* (c) Partition storage (q k N+1 ) and precipitation through bottom of box (P k N+1 ) based on thickness of model layer ( η) & estimated fall distance ( t V k ) P K N+1 8/10/2011 HFIP Physics Workshop 7

Other Assumptions Small cloud ice and large precipitation ice N SI ~ 10*N LI FLARGE=large/(small + large) => 0.1 (Hurr), 0.03 (NAM) Variable density for snow (similar to Morrison) 3D rime factor (RF) array for snow/graupel/sleet RF Deposition Accretion Deposition 1- m tables for ventilation, accretion, mass, & precipitation rates for liquid drops & ice (fast) 8/10/2011 HFIP Physics Workshop 8

Forecast satellite products (TOA radiances) 3-h NAM forecast water vapor channel 3 (6.5 m) FLARGE=0.1 Less small ice particles, warmer T b s (flagged by SPC) FLARGE=0.03 More small ice particles, cooler T b s (better) 8/10/2011 HFIP Physics Workshop 9

First-guess snow size (1 of 2) 0.01 [D] (mm) 0.1 (Extrapolated to -55ºC, [D]=50 m) Assumes (M-P) exponential spectra: N(D)=N o exp(- D), [D] = -1, N o - intercept, - slope, [D] mean D. 1 st guess is [D]=D o exp(-0.0536*t c ), T c in o C, D o =1 mm. Adjust [D] so N LImin N LI N LImax 1.0 10 (Ryan, 1996, 2000) HHHP (Washington state) SMPC (California) GM (California) PLATT (multiple locations) AWSE (Australia) YL (China) B, M (Europe) 8/10/2011 HFIP Physics Workshop 10

First-guess snow size (2 of 2) 0.01 [D] (mm) 0.1 1.0 10 (Extrapolated to -55ºC, [D]=50 m) Assumes (M-P) exponential spectra: N(D)=N o exp(- D), [D] = -1, N o - intercept, - slope, [D] mean D. 1 st guess is [D]=D o exp(-0.0536*t c ), T c in o C, D o =1 mm. Adjust [D] so N LImin N LI N LImax N LImin =1 L -1, N LImax =5 L -1 (from GFDL tuning runs in 2005-6) But in situ A/C observations of ice in hurricanes & tropical Cu have shown sometimes very high values for N LI! 8/10/2011 HFIP Physics Workshop 11

My Apologies Much of what follows is from NMMB model development It is not in the GFDL Hurricane Model, in the HWRF model, nor in WRF 8/10/20111 HFIP Physics Workshop 12

Recent Activities (NEMS/NMMB) Changes in new NMMB version of Fer Larger rain drops (expanded rain tables) Allow cloud ice (50 μm crystals) to fall slowly New cloud water to rain autoconversion (Liu & Daum) Faster falling rimed ice (~ V RF2 for V RF >1) Flag to control hydrometeor advection Advect q w, q r, q i or CWM only (F i, F r ) Applies to all schemes in NMMB (e.g., WSM6, etc) Incorporate aspects of GFS/Zhao into Fer? Cloud macrophysics for >O(10 km) grids 8/10/2011 HFIP Physics Workshop 13

1D Column Tests (1 of 3) Sample 1D input/output (thanks to B. Shipway, UKMO) Input: Updraft Forcing (specified) (Tropical West Pacific Input Sounding) RR max < 200 mm h -1 Old NAM Fer 8/10/2011 HFIP Physics Workshop 14

1D Column Tests (2 of 3) WSM6 R New NMMB fer R RR max >300 mm h -1 R R RR max > 300 mm h -1 Q: Why R WSM6? A: Used in 4-km NSSL/CAPS runs (Kain et al., HPC, 2009) => Of interest to SPC & HPC G R G 8/10/2011 HFIP Physics Workshop 15

1D Column Tests (3 of 3) WSM6 New NMMB Fer Old NAM Fer Much higher dbz & peak rain rates in New vs. Old Fer 8/10/2011 HFIP Physics Workshop 16

Impact of microphysics change Old NAM Fer New NMMB Fer 4-km CONUS nest runs using NMMB Higher composite dbz in revised version (right) 8/10/2011 HFIP Physics Workshop 17

Scientific Challenges (1 of 2) Higher resolution models (inner nests) Depends more on cloud physics details Riming, accretion becomes important (graupel, hail) Different ice habits m(d), V(D), shape effects Number concentrations, size spectra of hydrometeors Fundamental aspects of ice still not well known First initiation of ice, ice nucleation at cold temps, ice enhancement/multiplication (esp. tropical Cu!) Collection efficiencies between colliding ice species Huge range in costs & complexities of approaches Forecasts often limited by other error sources 8/10/2011 HFIP Physics Workshop 18

Scientific Challenges (2 of 2) Coarser grids (outer domain) & subgrid-scale cloud processes Shallow & deep convection Contrasting approaches between modeling groups More interactions w/microphysics for mass flux schemes (e.g., ncloud=1 in SAS) Partial cloudiness, cloud macrophysics GFS/Zhao-Moorthi uses Sundqvist condensation Validity of collection kernels questionable because of subgrid-scale variability 8/10/2011 HFIP Physics Workshop 19

Combine NAM & GFS Micro? Why? Some NMMB forecasts improved using GFS micro with GFS PBL + SAS Desire for physics unification (S. Lord) Useful for HWRF s outer 27-km domain Sundqvist condensation responds to moisture convergence (dt/dt, dq/dt) dp/dt=0, RH c =95% rather than 100% Used Sundqvist-based condensation and accretion processes in Ferrier scheme Sensitive to proper treatment of detrained condensate from SAS convection (ncloud=1) 8/10/2011 HFIP Physics Workshop 20

Equitable Threat Score (ETS) Preliminary QPF Results (12-km NMMB) (15 cases) Ferrier w/ GFS cond 8/10/2011 HFIP Physics Workshop 21

Radiation, cloud fractions Subtle differences between NAM & HWRF versions of GFDL SW, LW radiation Xu-Randall/Zhao cloud fraction (HWRF) vs PDF-based method (NAM) vs simple method (NMM B) Cloud absorption coefficients (old GFDL radiation units) HWRF, NAM Ops NAM NMMB Cloud Water 800 1600 800 Ice 500 1000 500 Larger cloud emissivities in NAM, NMMB In WRF/NAM code, LW fluxes are avg of T skin + T low, ; also avg ed LW cooling rates in lowest 2 layers Removed in the operational NAM (March 2008) 8/10/20111 HFIP Physics Workshop 22

Cloud Fraction Changes (1 of 4) High (%) Total ice (mm) Cloud Base (hpa) Cloud Top (hpa) The problem: too much overcast from high, very thin cirrus 8/10/2011 HFIP Physics Workshop 23

Cloud Fraction Changes (2 of 4) Total relative humidity, RH tot =(Q v +Q cld )/Q sat Cloud fraction (F c ) a function of RH tot, assumed to be Gaussian with =1% 97.7% / 99.9% 8/10/2011 HFIP Physics Workshop 24

Cloud Fraction Changes (3 of 4) Cloud water condensation Many CCN, droplet # conc O(10 2 10 3 cm -3 ) Water supersaturations rarely exceed 1% Vapor deposition onto ice Far fewer IN, crystal # conc O(1 10 3 L -1 ) Much higher SS ice at water saturation ~10% (-10 C), ~21.5% (-20 C), ~34% (-30 C), ~48% (-40 C) Ice saturation used for Q sat in F c at T<0 C RH tot»100% & F c 1 even when Q cld 0 8/10/2011 HFIP Physics Workshop 25

Cloud Fraction Changes (4 of 4) NAM: Original Cloud Fraction NMMB: Newest Cloud Fraction GOES W Vis Less upper-level cirrus (right) Simple fix for cloud fraction (Fc): F c =min[1, SQRT(10 5 *Q cld )] Better objective verification vs. CLAVRx & surface obs (not shown)

Community Challenges Managing multiple modeling systems (versionitis) HWRF, WRF, & NEMS community codes are complex Connections between physics ( wheel of pain ) e.g., SW sfc important for ocean & land models Do HWRF movable nest(s) complicate things? Thompson, WSM6 used to work in single domain runs in WRF V2.2 Do they work in HWRF V3.3 (e.g. Sam T s tests)? NCEP operations: optimizing costs & benefits Desire for more complex physics - vs - limited computing & human resources 8/10/2011 HFIP Physics Workshop 27