The TOSCA project

The TOSCA project
Towards an Optimalestimation Snow
Characterization Algorithm
DFG funded project
(German Science Foundation)
2008 - 2011
•
•
•
•
from the ground @ the UFS (=
Environmental Research Station
Schneefernerhaus, 2650m MSL)
from space
A. Battaglia (Univ. Bonn)
U. Löhnert (Univ. Cologne)
M. Hagen (DLR)
G. Peters (MPI Meteorology)
TOSCA: Motivation
No single instrument is solely capable of describing the
microphysical properties of snow!
combine many state-of-the-art remote sensing instruments into a
prototype of a physically consistent framework Î final goal:
Develop a modular optimal-estimation algorithm and evaluate the
potential for deriving columnar snow microphysics
If the evaluation of the TOSCA goal from the ground is negative,
it will be difficult to retrieve the snow from space at all!
Integration of atmospheric sensors @ UFS
DPR (RS passive): high accuracy LWP, ice microphysics
Disdrometer PARSIVEL:
In-situ fall speed, particle size
HATPRO (RS passive): T&q
profiles, LWP
Integration = physically consistent combination
of all employed measurement information
Need knowledge on:
• instrument characteristics (theory, error)
• forward model, i.e. radiative transfer
• inversion methods
Ceilometer (RS active):
backscatter profile
Cloud radar (RS active): cloud boundaries,
droplet fall velocity, cloud vertical structure
MicroRainRadar: Doppler velocity spectrum of
precipitation
Workshop questions addressed by TOSCA
Session “New Technology”, WG3/WG4
• TOSCA possesses something close to a “dream set” of instruments for retrieving
snow from the ground, missing instruments: 2D video-spectrometer, high power
lidar (depolarization, Raman technology) Î contributors still welcome for the
coming winter season!
• Within TOSCA we hope to be able to
¾ quantify how accurately parameters such as ice water content, snowfall rate
or PSD parameters can be retrieved
¾ estimate the influences of crystal habit and orientation on the retrieved
parameters
¾ understand which instruments contribute the most/least to the results – what
is the benefit of sensor synergy?
Session “Ground Validation”
1. & 4. TOSCA could fit into the proposed “physical validation”: influences of certain
assumptions concerning crystal habit, PSD and thus single scattering properties can
be examined in detail; sensitivity studies w.r.t. to the inclusion of different
instruments can be carried out
2. We plan to carry out such synthetic retrieval sensitivities using model output from
cloud resolving models (e.g. Lokal Modell, Meso-NH)
Impact of single scattering properties on TB
Sensitivity study (simulation):
• 5 km layer of snow with the lowest 3 km at constant rate and the upper 2 km
with linearly decreasing rate (Sekhon & Srivastava SD)
• atmosphere with lapse rates : 6K/km & 5%RH/km
• surface at 0°C & 100% RH and emissivity equal to 0.85
Descrimination of snow and ice signals
Sensitivity study (simulation):
• 5 km layer of snow with the lowest 3 km at constant rate and the upper 2 km
with linearly decreasing rate (Sekhon & Srivastava SD)
• atmosphere with lapse rates : 6K/km & 5%RH/km
• surface at 0°C & 100% RH and emissivity equal to 0.85
First measurements @ UFS (Jan. 27, 2008)
First measurements @ UFS (Jan. 27, 2008)
Exploit the role of ice in the microwave region
A. Battaglia, Univ. Bonn
• above ~90 GHz ice crystals are more and
more effective scatterers
• however single scattering properties strongly
depend on the size distribution & habit
Impact of single scattering properties on TB
Sensitivity study (simulation):
• 5 km layer of snow with the lowest 3 km at constant rate and the
upper 2 km with linearly decreasing rate (Sekhon & Srivastava SD)
• atmosphere with lapse rates : 6K/km & 5%RH/km
• surface at 0°C & 100% RH and emissivity equal to 0.85
A. Battaglia, Univ. Bonn
Large differences
between different habits