# Presentation: 604511

## GPM 

**for Operational **
**Numerical Weather Prediction**
**Peter Bauer**
- European Centre for Medium-Range Weather Forecasts (ECMWF)
- with contributions from:
**Philippe Lopez, Alan Geer, Sabatino Di Michele – ECMWF**
**V****éronique Ducrocq – CNRM****	****	****Rémy Montroty – Météo-France**
**Arthur Hou – NASA****	****	****	****Rossella Ferretti – U. L’Aquila**
**Russ Treadon – NCEP****	****	****Kostas Lagourvados – NO Athens**
**Sue Ballard – UK Met Office**

##  Bayes’ theorem:

**System**

**Background**

**+****Errors**

**Observations**

**+****Errors**

**Analysis**

**+****Errors**

**Operator**

**+****Errors**

** ****Bayes’ theorem:**

**Data assimilation problem**

**(Courtesy F. Chevallier)**

## Microwave imager data: Humidity analysis in cloud-free areas

- EUMETNET Composite Observing System (**EUCOS**)
- Observing System Experiments (**OSE**):
-  ECMWF operational 4D-Var system
-  T511/L60 12-hour
-  **Control**: operational analysis
-  **Baseline**: all conventional observations used in  NWP (radiosonde+aircraft+profiler network+
-   surface land+buoy+ship data)
-  **Reference**: Baseline+Atmospheric Motion   Vectors (AMVs) from GEO+MODIS

**Forecast RMSE: **
**850 hPa rel. humidity**

**Forecast RMSE: **
**500 hPa rel. humidity**

**Forecast RMSE: **
**200 hPa vector wind**

**(Kelly and Th****épaut**** 2006)**

## Model issues: Physics

- Comparison of forecasts from 5 CRMs (at ~1.25 km resolution)
- for a TOGA-COARE squall line

**Evolution of surface precipitation     **

**Hydrometeor profiles**

**6h total amounts of hydrometeors**

**(Redelsperger et al. 2000)**

## Model issues: Physics+Resolution

**(Ebert et al. 2003)**

- WGNE assessment of short-term
- quantitative precipitation forecasts:
- Relative error contribution	       Tropics, Summer			SE, Winter
- for rain systems
- over Australia

## Model issues: Physics+Resolution

- UM

- ARPEGE

- Méso-NH

**Integrated cloud+precipitation water contents [kg/m****2****]:**
**UM: ****	****UK Met Office unified model**
**ARPEGE: ****	****M****été****o-France global model**
**M****éso-NH: ****	****CNRM non-hydrostatic regional model**
**12-hour forecast initialized on 17/02/1997 at 00 UTC**
**Resolution: UM, Méso-NH 11km, ARPEGE 30 km**

## Sensitivity: Time evolution of Jacobians 2D-Var

-  The weight given to rain-gauge observations during the 2D-Var minimization decreases in time through the assimilation window.

**Maximum sensitivity of simulated 3-hour accumulated precipitation to initial T and q **
**over 2D-Var 12-hour assimilation window (from rain-gauge assimilation exp.)**

**Sensitivity: Time evolution of Jacobians 2D-Var**

**(Courtesy P. Lopez)**

## Sensitivity: Time evolution of Jacobians 3D-Var AD-integration

- and MSLP
- 48-hour T95L60 forecast started
- at 1200 UTC 12 September 2003.

**Time (h)**

- TC Isabel

- 1200UTC 14 September 2003

**Sensitivity: Time evolution of Jacobians 3D-Var AD-integration**

**(Courtesy P. Lopez)**

## Data assimilation implementations

**Physical initialization:**
- 	Donner (1988): experimental, global, CCM0B
- 	Krishnamurti et al. (1991): experimental, global, FSU T106L14
**Nudging (latent heating):**
- 	Heckley et al. (1990): experimental, global (tropical), ECMWF
- 	MacPherson et al. (1996): *operational*, regional, UKMO
- 	Wang and Warner (1988): experimental, regional, MM5
- 	Ducrocq et  al. (2000): experimental, regional, Méso-NH
- 	Lin et al. (2002): experimental, regional, NCEP
**Variational techniques (3D/4D-Var):**
**	****Incremental***
- 		Mahfouf and Marécal (2002): experimental, global, ECMWF T511L60
- 		Treadon (2003): *operational*, global, GDAS, T254L64
- 		Koizumi et al. (2005): *operational*, regional, JMA, MSM
- 		Bauer et al. (2006): *operational*, global, ECMWF T799L91
**	****Non-incremental**
- 		Zupanski et al. (2002): experimental, regional, NCEP ETA
- 		Zou and Kuo (1996): experimental, regional, MM5
- 		Tsuyuki (1997): experimental, global, FSU T42L10
- 		Sun and Crook (2001): experimental, local, MM5/WRF
- 		Vukicevic et al. (2004): experimental, regional, RAMDAS
- 	**Other**
- 		Hou et al. (2002, 2004), experimental, global, GEOS
*** only affordable system for global operational applications: ECMWF, NCEP, JMA, MSC, UKMO, M****étéo-France**

## Global operational systems: GDAS

**(Courtesy R. Treadon)**

**NCEP Global Data Assimilation System (GDAS), 3D-Var:**
**Products assimilated**
- 1o superobs of SSM/I (FNMOC) and TMI (2A12) precipitation rates
- 02/21/2001 – operational use of SSM/I
- 10/16/2001 – operational use of TMI
**Impacts**
- Greatest impact on reducing areas of spurious precipitation
- Reduction in overall impact as increase amount of assimilated radiance data
**Future direction**
- Moving towards radiance assimilation with cloudy fields of view
- 	          **GFS** **T254L64**
**	****           ****Rain - NoRain**

## TCWV increments 1-cycle, Rain - NoRain: 2005080100

**1D+4D-Var Assimilation system (operational since June 2005)**

**Direct 4D-Var Radiance assimilation system (experimental)**

**[kg m****-2****]**

**Operational system**
**+**
**1D-variational retrieval**
**of TCWV in clouds+rain**
**with SSM/I radiances**
**+**
**4D-variational assimilation**
**of TCWV**
**Operational system**
**+**
**4D-variational assimilation**
**of SSM/I radiances**
**in clouds+rain**

## Mean Relative Humidity Forecast RMSE Difference 08-10/2004

**90% Statistical significance      Improvement          ****	****    Deterioration**

**Mean Relative Humidity Forecast RMSE Difference 08-10/2004**
**Rain – No Rain**

**Global operational systems: ECMWF**

## Global experimental systems: GEOS

- Improved MJO signals in GEOS analysis over tropical oceans (10N-10S): Jan-Dec 2001

- Precipitation assimilation using the VCA procedure in the GEOS DAS replicates the observed MJO rainfall patterns

- Blue: Control forecast
- Red: Forecast with TMI+SSM/I rainfall data in initial condition
- Green: NOAA best track

- Improved hurricane forecast:               Hurricane Bonnie Experiment

- Rain Threshold (mm/day)

- Day-3 precipitation threat score

- 5-day track forecast from 12UTC 8/20/98

**Global experimental systems: GEOS**

**(Hou et al. 2004, 2006)**

## Regional experimental systems: UK Met Office

**Regional experimental systems: UK Met Office**

**Accumulated precipitation (mm)**
**19-20 UTC 27****th**** April 2004**
**from 18 UTC analysis**

- MOPS
- 			    Radar
- No MOPS

- UK Met Office model suite:
- Global, 40 km, 4D-Var
- Europe, 12 km, 4D-Var
- UK, 4 km, 3D-Var (FGAT+RH, latent heat nudging)

**(Courtesy S. Ballard)**

## Regional experimental systems: CNRM

**Extreme flash-flood event over Southern France in September 2002**
**12-h accumulated rainfall from 12 UTC, 8 Sept to 0 UTC, 9 Sept 2002**

**Aladin**
- 9.5 km resolution;
- parameterized  convection;
- 1 prognostic water variable (water vapour).

**Meso-NH/Arome **
- 2.5 km resolution
- explicit convection;
- advanced microphysic scheme: 6 prognostic water variables (water vapour, cloudwater, rainwater, snow, graupel, primary ice);
- mesoscale data assimilation.

**Regional experimental systems: CNRM**

**(Courtesy V. Ducrocq)**

## Regional experimental systems: CNRM

**+**

- Nîmes

- Nîmes radar

- Initial conditions : Large-scale ARPEGE analysis for
- 12UTC, 8th Sept. 2002

**+**

**12-h accumulated rainfall **
**08/09/2002 12 UTC – 09/09/2002 00 UTC **

- Gauge network

**Regional experimental systems: CNRM**

**(Courtesy V. Ducrocq)**

**Notes:**
All the pannels show 12 h accumulated surface rainfall totals from 12 UTC the 8th to 00 UTC the 9th September 2002 (same colour scale for the simulations and the raingauges, almost the same for the radar) 
1) The right column : observations (maxima of precipitation about ~300 mm) 
Top : raingauges (METEO-France + DDE Networks) superimposed to the orography 
Bottom : accumulated rainfall from the Nîmes radar (adjustment  based on the raingauges)
2)The left column : 2.5 km MESO-NH simulations starting from two different initial conditions at 12 UTC, 8 September 2002
Top : the initial conditions are provided by the  mesoscale initialization of Ducrocq et al (2000), that combines:
an Optimal interpolation of mesonet surface observations (surface observations are spaced in average by 30 km)
An ajustment of the humidity and hydrometeors based on the radar reflectivities and the METEOSAT Infrared brightness temperature
Bottom : the initial conditions are provided by the large scale 4D-Var operational ARPEGE analysis. 
The boundary conditions are the same than those of the operational  ALADIN system : they are provided by the 3-hourly ARPEGE forecast starting at 12UTC
Comments on the bottom model figure :
Using a higher resolution and more advanced physical parameterizations improves the quantitative precipitation forecast (Maximum of precipitation is 250 mm in 12 hours)
.  Comparison on the same scale as the Aladin grid-mesh  shows that the maximum of rainfall is multiplied by about a factor 2  in the 2.5 km MESO-NH forecast with respect to the ALADIN operational forecast based on the same ARPEGE analysis (not shown).  However,  both the  ALADIN and MESO-NH starting from the large scale ARPEGE analysis place the maximum of precipitation over the Massif Central crests and not over the Gard area. 

Comments on the top model figure (Maximum of precipitation is 260 mm in 12 hours):
 Starting from a mesoscale data analysis as initial conditions  is more efficient to improve the short range forecast, indeed the assimilation of mesonet surface observations, radar and satellite data  helps to locate correctly the  maxima of precipitation. 

## Météo-France

**(Courtesy R****é****my Montroty)**

## National Observatory of Athens

_**Assimilation run**_**: 6-hour accumulated precipitation (at 10 mm interval) valid at (a) 1200 UTC 5 December 2002, (b) 1800 UTC 5 December 2002 and (c) 0000 UTC 6 December 2002 (a) as predicted by MM5 Grid 2.**

_**Control run**_**: 6-hour accumulated precipitation (at 10 mm interval) valid at (a) 1200 UTC 5 December 2002, (b) 1800 UTC 5 December 2002 and (c) 0000 UTC 6 December 2002 (a) as predicted by MM5 Grid 2.**

**MM5-CNTRL: 66 mm**
**OBS: 122 mm**

**MM5-ASSIM: 91 mm**
**OBS: 122 mm**

**National Observatory of Athens**

**The 4-6/12/2002 Antalya-Turkey case: Humidity adjustment of initial conditions based on satellite estimated precipitation      Kostas Lagourvados**

**(Courtesy Kostas Lagourvados)**

## University L’Aquila et al.

**University L’Aquila et al.**

**(Courtesy Rossella Ferretti)**

**Assimilation of conventional/non-conventional data in MM5:**
**3 two way nested domains at 27km (D1), 9km (D2), and 3km (D3)**
**GPS ZTD (3DVAR/FDDA), SSMI and SYNOP+SOUNDINGS**
**CASE:  FLOOD in SICILY 16 September 2003**

## University L’Aquila et al.

**University L’Aquila et al.**

## Data assimilation trade-off for instrument definition

*“**Derive post-EPS mission requirements related to the observation *
*of clouds and precipitation with a polar microwave radiometer”*

**Project:**
-  Identify those microwave frequencies between 5-200 GHz that are optimally suited for cloud and precipitation remote sensing (and that are protected by International Telecommunication Union, ITU, regulations).
-  Consider ocean/land surfaces and all weather conditions.
-  Develop framework for the estimation of potential hydrometeor retrieval accuracies given the identified channel selection.
-  Assess retrieval accuracy based on user requirements defined for post-EPS.

**P. Bauer and S. Di Michele, ECMWF**

## Information Content

- Signal to noise can be characterized as x/ or x / with:
- 	x	atmospheric/surface variable
- 	x	standard deviation of x’s variability
- 		noise
- Information content of a measurement = factor of x-knowledge improvement when
- 				making observation(s) [often as *log (factor)*]
- Linear Gaussian case:
- 		Hs = **S **[P(**x**)] – **S** [P(**x**|**y**)] 	*Entropy reduction*
- 		**A** = Analysis error covariance matrix
- 		P = (joint) pdf of **x**(**y**)
- Iterative method for channel optimization:
- 		**A**-1 = **B**-1 + **hh**T 		Improvement of **A** over **B** with **H**
- 		**B** = Background error covariance matrix
- 		**H** = Jacobian matrix with columns **h**, normalized with** R**
**	****	****R = **Observation+Modelling error covariance matrix
- 	1. calculate Hs for all channels and select highest
- 	2. update **B** with **A**
- 	3. calculate Hs for remaining channels and select highest

**P. Bauer and S. Di Michele, ECMWF**

## Ingredients

**x:****	**- (T,q)-profiles from short-range ECMWF model forecasts,
- 	- hydrometeor profiles from application of observation operator,	- surface emissivities from modelling (ocean) or climatologies (land).
**H**:	- linearized large-scale condensation + convection scheme,
- 	- multiple scattering radiative transfer model (conical scanner 	  ensuring constant viewing geometry and polarization usage).
**B**:	- for (T,q) from operational model formulation,
- 	- for hydrometeors from HBHT.
**R**:	- HBHT calibrated with results from observational method.
**Objective**:
- Identify channels that provide highest information content in most of the cases
*Normalize Entropy reduction with number of cases for which reduction exceeds a threshold*

**P. Bauer and S. Di Michele, ECMWF**

## Definition of passive microwave imager specs for post-EPS

**AMSR-E+**

**Rain****	****	****	****	****         Snow**

-  Eumetsat Polar System (EPS) follow-on, 2020+
-  Study on dedicated specifications for clouds and precipitation: channel selection
-  Channel identification and hydrometeor retrieval accuracy estimation   (also with AMSR-E as baseline)

- (Channel priorities for land/ocean surfaces, global profile datasets, optimal estimation theory, x-axis: mean entropy reduction)

**Definition of passive microwave imager specs for post-EPS**

**P. Bauer and S. Di Michele, ECMWF**

- ocean	       land

- ocean	       land

## Issues

**Model component/experiment evaluation**

** ****Precipitation specific scoring **
** ****Analysis system performance**
** ****Independent, system-wide, ****    satellites (radiance)**
**	****5%**_*** ***_

** ****Sub grid-scale processes**
** ****Consistency b/w NL, TL, AD**
** ****Sensitivity w/r/t control   ****     variables**
**	****30%**

** ****Choice of observable: RR vs. TB/Z**
** ****Observation+modelling errors **
** ****Accuracy vs. efficiency**
** ****Regularity/linearity**
**	****  ****5 %**

** ****Operational vs. experimental**
** ****Spatial/temporal scales**
** ****Balance b/w moisture & dynamics**
** ****Model errors (background), ****    spatial structure**
**	****  ****50%**

** ****Operational vs. experimental**
** ****Objective, statistically significant**
** ****Cases where moisture analysis critical**
** ****Consistency b/w impact of clear-sky **
**    ****AND cloud/precip observations**
**	****    ****10%**

**       ****Physical**
**parameterizations**
**Observation**
**operators**
**  ****Analysis system****	****  Regional model**
**	****	****initialization/forcing**
**      ****Data assimilation experimentation**
**  ****Land surface analysis system/modelling**

## Expected developments relevant to GPM-era

**ECMWF now:   ****	****- 25 km, 91 model levels (T799L91);**
**                   ****	****- 2 analysis suites (6-hour, 12-hour window);**
**                   ****	****- 2 10-day forecasts initialized at 00 and 12 UTC;**
**                   ****	****- 50-member EPS (65 km, T319);**
**                   ****	****- Radiances/products from ~20 different satellite sensors assimilated;**
**                   ****	****- Assimilation of rain-affected radiances operational since 28/06/2005.**
**ECMWF ~2010-15: - deterministic 15 km, EPS 30 km;**
**                   ****	****- towards longer assimilation window analyses;**
**                   ****	****- towards unified ensemble prediction systems ****	****	****  ****	****(medium-range, monthly, seasonal);**
**                   ****	****- towards coupled data assimilation (land-ocean-atmosphere);**
**                   ****	****- towards environmental monitoring;**
**                   ****	****- towards focus on severe weather forecasting.**
**General:****	****	****- NWP-systems will become much better in (physically) resolving even****	****	****             meso-scale synoptic systems.**
**            ****	****	****- NWP-systems will become much better in assimilating cloud and rain**** ****	****	****             affected observations (see recent JCSDA workshop)**
**Regional NWP:****	****- Cloud/precipitation observations will become standard observations**
**              ****(NWC)****	****	****for constraining moist physics on short time-scales.**
**	****	****- Deterministic/ensemble model cascades:**
**	****	****	****global NWP – regional NWP - hydrology**

**GPM data must be made available in near real-time **
**(even more stringent for meso-scale modelling with short cut-off times) **