Future Guidelines on solar

forecasting: the meteorological view

Isabel Martínez Marco imartinezm@aemet.es

AEMET

In collaboration with Emilio Cuevas, Pilar Fernández, Enric Terradellas and Javier Calvo

Outline

• Introduction

• Nowcasting: • Cloud and irradiance nowcasting from Total-Sky cameras

• SAF of Nowcasting (NWC SAF)

• Forecasting: • HIRLAM and HARMONIE Models

• ECMWF Model

• Quick overview of the MACC/ECMWF aerosol analysis and

forecasting system

• WMO SDS-WAS program

• Dust forecast

3

Nowcasting is a technique for very short-range forecasting (normally

within 6h ahead) covering only a very specific geographic region.

In cloud nowcasting we map the current cloudiness and, using an

estimate of its speed and direction of movement, we forecast the

cloudiness a short period ahead (1-2h) for a specific site (1 km2) —

assuming the weather will move without significant changes.

10 20 30 40 50 60 70 80 90 100 120 130 140

150 minutes

Nowcasting

Forecasting In-situ observations

Satellite

Neural network models Satellite information

Post-processed Numerical Weather Prediction Model Data

Nowcasting concept

4

Total sky imagery can be used to make forecasts in quasi-real time (with a delay

of only 15-30 minutes) by applying image processing and cloud tracking techniques

to digitized sky photographs. Hazy skies can make difficult to properly identify

clouds.

Under cloudless skies, when most Concentrated Solar Power (CSP) and

Concentrated Photovoltaics (CPV) plants operate, aerosol optical depth (AOD)

becomes the driver factor. Large portion of uncertainty can be attributed to the lack

of accurate aerosol data used to model DNI.

Satellite imagery applies total sky imagery methods to cloud scenes (e.g. the

SEVIRI cloud-motion winds derived from successive satellite images can be used

to predict the DNI at ground level with sufficient accuracy.

DNI and GHI attenuation by different types of clouds and aerosols must be

parameterized by sensitivity studies using Radiative Transfer Models (RTF).

So, a combined approach using in-situ observations (total-sky cameras and

radiometers), satellite observations (SEVIRI), and RTF models appears to be able

to provide the most accurate results for cloud-DNI-GHI nowcasting at CSPs and

CPVs

Main considerations

5

- CCD sensor. 640x480 pixels, 8 bit, color response from 400 to 700nm and monochrome response from 400 to 1000nm.

- Very durable aluminium housing.

- Borosilitate dome.

- Rotating shadow band.

- Cooling/heating system (-10º to +50ºC).

- Fast frame rates (up to 70 fps).

- Adjustable JPEG compressed still-images or live MJPEG streaming video.

- Transfer of images via FTP, RTP or HTTP.

- Camera control via HTTP, XML-RPC, Telnet

Component#1: SONA Cloud Observation Automatic System

6

Cloud detection: Neural network

Cloud flow determination:

To cluster the motion field we have

based on a Density-Based Algorithm

for Discovering Clusters in Large

Spatial Databases with Noise

(DBSCAN)

7

Component #2: In-situ column aerosol content determination

Preliminary (excellent)

results of total column

aerosol content obtained

with a new inexpensive CI

radiometer

compared with AOD from

AERONET

8

Cloud Mask Cloud Type

Cloud Top Height Cloud Top Pressure

Component#3: NWC SAF cloud products

9

Component#4: Cloud and dust sensitivity analysis with neural network models and LibRadtran

DNI attenuation by altocumulus

GHI

DNI

DHI

10

Summarizing the nowcasting state of the art

Neural network

modeling

DNI/GHI nowcasting

Cloud observation

DNI, GHI observation

Aerosol/dust observation

Optical flow

Cloud height/type

DNI/GHI

Cloud attenuation

Aerosol, Dust and water vapor

sensitivity analysis

SAF/NWC Development

of low-cost

instruments

SAF de Nowcasting

(NWC SAF)

The Nowcasting Satellite Application Facility (SAF) was established in 1996 between EUMETSAT and former Instituto Nacional de Meteorología (Spanish National Weather Service, AEMET (Agencia Estatal de Meteorología) since 2008).

Under the leadership of the Spanish Meteorological

Agency (AEMET), the NWC SAF is developed by a

Project Team involving France (Météo-France), Sweden

(SMHI) and Austria (ZAMG) Meteorological Services.

Objectives

Development of Nowcasting products derived from both

Geostationary (MSG) and Polar Platform (PPS) satellite

systems

To be delivered to users as SW Packages

Products are generated locally at user premises

Responsible for

Development and maintenance of the NWC products

Development and maintenance of the SW Packages

User's support and training tasks

89 users at the date http://www.nwcsaf.org

Clear Air, Precipitation & Wind MSG products

Clouds & Convection MSG products

Cloud & Precipitation PPS products

Meteorological Systems MSG products

Products responsibility

Cloud Products MSG

Cloud Top Temperature & Height (CTTH)

Detailed cloud analysis with information on

the major cloud classes for all the pixels

identified as cloudy.

Information on the cloud top temperature,

pressure and height for all pixels identified

as cloudy.

Cloud Type (CT)

Cloud-free pixels delineation in a satellite

scene with a high confidence.

Also: snow/sea ice, dust clouds and

volcanic plumes.

Cloud Mask (CMa)

Cloud Products PPS

Cloud Type (CT) Cloud Mask (CMa) Cloud Top Temperature &

Height (CTTH)

Precipitation & Convection Products (MSG&PPS)

Precipitating Clouds (PC)

Probability of precipitation intensities in pre-

defined intensity intervals.

Convective Rainfall Rate (CRR)

Precipitation estimated rate associated to

convective clouds. Instantaneous rain rate

and hourly accumulations.

Rapid Development Thunderstorm (RDT)

Identification, monitoring and tracking of

intense convective systems, and detection of

rapidly developing convective cells

PPS MSG

Clear Air Products MSG

SAFNWC Physical Retrieval (SPhR)

Optimal estimation algorithm to obtain Stability Parameters:

Total and Layered Precipitable Water and Instability Indexes

TPW LPW-HL LPW-ML LPW-BL

LI KI SHW

HRW v2011

HRW v2012

Up to 7 SEVIRI Channels

Improved Height assignment

Wind Products MSG

Detailed and frequently updated

sets of Automatic Motion Winds

including wind pressure level

information and quality control

flags.

19

10 20 30 40 50 60 70 80 90 100 120 130 140

150 minutes

Nowcasting

Forecasting In-situ observations

Satellite

Neural network models Satellite information

Post-processed Numerical Weather Prediction Model Data

Forecasting

Numerical weather prediction

• The behaviour of the atmosphere is governed by a set of physical laws which express how the air moves, the process of heating and cooling, the role of moisture, and so on.

• Equations cannot be solved analytically, numerical methods are needed.

• Given a description of the current state of the atmosphere, numerical models can be used to propagate this information forwards to produce a forecast for future weather.

• Additionally, knowledge of initial conditions of system is necessary.

• Incomplete picture from observations can be completed by data assimilation.

• Resolution of the model is determined by available computing resources. It does not correspond to any natural scale separation.

Numerical Weather Prediction

• Processes not resolved by the model must be ‘parametrized’.

• Effective resolution is not same as model grid spacing.

• Numerical algorithms are compromise between accuracy and speed; care

needed to ensure numerical stability.

• Interactions between atmosphere and land/ocean important

Forecast ranges

• Short-range weather forecast (0-2 days ahead)

• Detailed prediction - regional forecasting system

• Produce forecast few hours after observations are made

• Medium-range weather forecast (2 days - 2 weeks ahead)

• Less detailed prediction - global forecasting system

• Produce forecast up to several hours after observations are made

• Long-range weather forecast (more than 2 weeks ahead)

• Predict statistics of weather for coming month or season

• Climate prediction

• Predicts the climate evolution on the basis of pre-defined scenarios (CO2, O3, …)

HIRLAM (High Resolution Limited Area Model)

• Model Formulation:

• Horizontal resolution: 0.16º latxlon (ONR) and 0.05º latxlon (HNR)

• Boundary Conditions:

• ONR: from ECMWF with 0.25º

• HNR and CNN: from ONR with 0.16º (nesting models)

• Analysis: 3-dimensional variational method (3D-VAR)

• The Resolution in space

• Vertical Resolution: 40 hybrid levels

• Horizontal grid: regular rotated longitude/latitude

Integration Domains

HIRLAM (High Resolution Limited Area Model)

• In development: HARMONIE

Hirlam Aladin Regional/Meso-scale Operational NWP In Europe

• A new model formulation:

• Horizontal resolution: 2.5km

• Vertical resolution: 65 hybrid levels

• Analysis: 4-dimensional variational method (4D-VAR)

• Horizontal grid: Spectral representation

• Vertical grid: finite differences

• Non-hydrostatic dynamical kernel from ALADIN Model

The ECMWF Numerical Weather

Prediction (NWP) Model

• High-resolution model

• T1279 spectral resolution

• 16 km global grid

• 91 hybrid levels from the surface to a height of 80km

• Variables at each grid point

• Wind

• Temperature

• Humidity

• Cloud water, ice, cloud fraction

• Ozone

• Pressure at surface

• A number of radiation schemes are in use at ECMWF. As of January 2011 are

active

• McRad including RRTM_LW and RRTM_SW is used in the forward model for

operational 10-day forecasts at TL1279 L91, EPS 15-day forecasts at TL639

L62, and seasonal forecasts at TL159 L62.

• The tangent linear and adjoint of the “old” SW radiation scheme in a 2-

spectral interval version is used for Data Assimilation.

• The tangent linear and adjoint of the “old” LW radiation scheme with 6

spectral intervals, replacing a neural network version of the same “old” LW

radiation scheme (Morcrette, 1991; Janiskova and Morcrette, 2005), is used

for DA.

• … and all the dedicated RT scheme used to simulate radiances (RTTOV-

based) in the analysis of satellite data.

The ECMWF radiation schemes

The ECMWF radiation schemes

Adiabatic processes

Winds Temperature Humidity Cloud Fraction

Cloud Water

Diffusion Radiation Cumulus

convection Stratiform

precipitation

Friction Sensible

heat flux

Evaporation

Ground

roughness

Ground

temperature

Snow Ground

humidity

Snow

melt

The ECMWF radiation schemes

• Differences with other physical processes • There exists a well known theory (from Quantum Mechanics to

Spectroscopy to Radiation Transfer).

• Radiation is exchanged with the outside space: radiative balance

determines the climate.

• The sun providing the energy input, radiation undergoes regular

forcings: seasonal, diurnal.

• Radiation at ToA has been globally measured since the 60’s (by

operational satellites), with real flux measurements from ERB (1978),

ERBE (1985), ScaRaB (1993), CERES (1998).

• Surface radiation has been (roughly) measured at points over almost 40

years. Present programs like ARM, BSRN, SURFRAD measure it with high

accuracy. Also satellite-derived SW (and LW) radiation is becoming

available.

• Therefore, there exist some relatively extended possibilities of

validation/verification (radiation in the SW visible and near-IR, in the

LW, … in the mW).

The ECMWF radiation schemes

• What is required to build a radiation transfer scheme for

a GCM?

• 5 elements, the last, in principle in any order:

• a formal solution of the radiation transfer equation

• an integration over the vertical, taking into account the

variations of the radiative parameters with the vertical

coordinate

• an integration over the angle, to go from a radiance to a flux

• an integration over the spectrum, to go from monochromatic to

the considered spectral domain

• a differentiation of the total flux w.r.t. the vertical coordinate

to get a profile of heating rate

The ECMWF radiation schemes

• In the ECMWF model, the 3-D distributions of T, H2O, cloud fraction

(CF), cloud liquid water (CLW), cloud ice (CIW) are given for every

time-step by the prognostic equations.

• Other parameters, i.e., O3, CO2 and other uniformly mixed gases of

radiative importance (O2, CH4, N2O, CFC-11, CFC-12 and aerosols)

have to be specified (prognostic O3 soon interactive with rad?).

• Prognostic aerosols (as part of GEMS/MACC project)

Radiation black

box

Efficient radiation

transfer

algorithms

Profiles of T,

q, CF, CLW,

CIW,

O3

Climatological

data:

other trace

gases, aerosols

OUTPUT

updated

from

time to time

to be used in

the

thermodynamic

equation

DFLW, DFSW to be

used in the surface

(soil) energy

balance equation

Rad

t

T

MACC Daily Service Provision

Air

quality

Global

Pollution

Aerosol UV index

Biomass

burning

http://www.gmes-atmosphere.eu

Radiation Transfer in NWP: Lecture 4

Forward modelling of aerosols

• As part of the GEMS/MACC/MACC II projects, the IFS has been modified to include

prognostic aerosols (sea-salt SS, dust DU, organic OM and black carbon BC, sulphate

SO4).

• Sources for SS and DU are linked to some of the model surface parameters (U10,

soil moisture, UVis albedo, stdev orography, snow mask).

• Sources for OM, BC and SO4 are taken from climatologies and/or inventories

(GFEDm, GFED8d, SPEW, EDGAR databases). For NRT FCs, OC, BC and SO4 linked to

fire emissions are linked to an analysis of the MODIS and Geostationary Satellites

“fire hot spots”

• Aerosols are transported by advection, vertical diffusion and convection, and

undergo their specific processes, i.e., sedimentation, dry deposition, wet

deposition by large-scale and convective precipitation, and for OM and BC

hygroscopic effects. Transfer between SO2 and SO4 is handled with a time-scale

simply dependent on latitude.

• The TL159 (GEMS) and the TL255 (MACC) L60 models have been simulating aerosols

for the 2003-2008 (GEMS) and 2003-2010 (MACC)-AER reference period. Since

September 2008, an experimental pre-operational near-real time aerosol analysis

followed by a 5-day FC is produced every day.

• Comparisons with MODIS and AERONET data.

http://www.gems-atmosphere.eu/d/services/gac/nrt/nrt_opticaldepth/

Prognostic AERosols in the ECMWF IFS

Prognostic AERosols in the ECMWF IFS

• Aerosol model to represent the main characteristics of the 4D distribution of aerosols, while keeping the computational burden within the parameters of a future operational configuration.

• Aerosol model formulation originally taken from the LOA/LMD-Z model (Reddy et al., 2005, JGR), and adapted to the IFS

• Adapted to the ECMWF IFS model dynamics and physics: • with original developments to include N (=12) new prognostic variables for

the aerosols • and original developments/upgrades to the sedimentation, wet deposition

and radiative diagnostics.

• Extensive validation against MODIS t550 (aerosol optical depth at 550 nm), AERONET t500, t865, CALIPSO aerosol/cloud mask

• ECMWF IFS model including prognostic aerosols can be run in two

configurations:

• In aerosol free-wheeling mode: aerosol advection and “full” (but simplified) aerosol physics using temperature, humidity, winds etc. from the analyses/forecasts every 12 hours

• In analysis mode with subsequent forecasts

Quick overview of the MACC/ECMWF

aerosol analysis and forecasting

system

•

•

•

•

Forward model Analysis

Evaluation with MODIS/SEVIRI and AERONET Saharan dust outbreak: 6 March 2004

Model simulation Assimilation MODIS

SEVIRI

Cape Verde Dakar AERONET

Assimilation

Simulation

Aerosol optical depth at 550nm (upper)

and 670/675nm (lower)

Comparison of GEMS simulated and analysed aerosol optical depth with MODIS and MISR for July 2003

WMO SDS-WAS programme Regional Center for

Northern Africa, Middle East and Europe

http://sds-was.aemet.es

sdswas@aemet.es

• WMO SDS-WAS program • Dust forecast

WMO SDS-WAS program

Mission: Improve the capacity of countries to produce and deliver to end users timely and precise atmospheric dust forecasts Structure: •Regional Center for Northern Africa, Middle East and Europe. Barcelona, Spain •Regional Center for Asia, Beijing, China •Regional Center for Pan America, Orange, Ca, USA

The Regional Center is managed by the Spanish Met. Agency (AEMET) AND THE Barcelona Supercomputing Center (BSC-CNS)

Nexus II building Catalonia Tech. University MareNostrum supercomputer

The Regional Center NA-ME-E

http://sds-was.aemet.es

sdswas@aemet.es eterradellasj@aemet.es

Forecast products

MODEL INSTITUTION RUN

TIME

DOMAIN DATA

ASSIMILATION

BSC-

DREAM8b

BSC-CNS 12 Regional No

CHIMERE LMD 00 Regional No

LMDzT-INCA LSCE 00 Global No

MACC ECMWF 00 Global MODIS AOD

DREAM-

NMME-MACC

SEEVCCC 12 Regional MACC analysis

NMMB/BSC-

Dust

BSC-CNS 12 Regional No

MetUM U. K. Met

Office

00 Global MODIS AOD

GEOS-5 NASA 00 Global MODIS

reflectances

NGAC NCEP 00 Global No

Dust models

Dust optical depth at 550 nm

RUN: 15 Apr 2013

VALID: 15 Apr 2013 12:00 – 18 Apr 2013 00:00

Surface concentration

RUN: 15 Apr 2013

VALID: 15 Apr 2013 12:00 – 18 Apr 2013 00:00

Numerical products

netCDF format

SFC

concentration

Dust AOD 550 µm

Evaluation with AERONET data

SANTA_CRUZ_TENERIFE

Evaluation with satellite products

24 Apr 2013