Remote sensing with FAAM to evaluate model performance

Transcription

1 Remote sensing with FAAM to evaluate model performance YOPP-UK Workshop Chawn Harlow, Exeter 10 November 2015

2 Contents This presentation covers the following areas Introduce myself Focus of radiation research within OBR Example investigations from past FAAM campaigns Summary

3 Who is Chawn Harlow? OBR = Observation-Based Research Unit within Met Office that collects and analyses research-grade observations from FAAM aircraft and from the Cardington ground-based facility. Manager of OBR s Radiation Group Lead on several past radiation campaigns using FAAM Chair of FAAM Radiation and Remote Sensing Working Group.

4 The Facility for Airborne Atmospheric Measurements Ceiling: 35,000 ft Duration: 5 hours MW and IR radiometers Broad-Band Radiometers

5 Focus of radiation research within OBR Remote sensing of surface emissivity and skin temperature at IR and MW wavelengths. Evaluate surface temps predicted with the UM Evaluate surface emissivity models used in satellite DA. Retrieval and modelling of ice and dust optical properties in VIS, TIR and MW (up to 900 GHz) retrievals of particulate and thermodynamic profiles. modelling of the Earth s radiation budget. Evaluation of clear skies spectroscopy in GHz Necessary to obtain best results with ICI Development of radiative transfer models valid across MW to VIS PC-based radiative transfer model, HT-FRTC.

6 Examples investigations from FAAM campaigns Diagnosis and rectification of LST biases using SALSTICE data (recent work with Jenn Brooke) Sensitivity of passive microwave emissivities to snowpack properties (based on Harlow (2011))

7 Diagnosis and rectification of LST biases using SALSTICE data

8 Note: This is a land-only product. Surfacesensitive channels are black-listed over sea-ice and land where errors are likely to be large due to poor knowledge of surface temperatures and emissivity. Global LST O-B Sept Aug SALSTICE campaign

9 Aircraft LST Evaluation ARIES Footprint FOV = km 2 at 3,000 m above the surface

10 MODIS Comparisons MODIS Collection 5 1 km LST product. Mean bias: K. High degree of variability in LST biases which is related to heterogeneity in surface vegetation. The bare soil cover fraction is too low across the whole region. Regions of large LST bias are associated with low bare soil cover fraction during the day. The LST bias is negatively-correlated with the bare soil cover fraction during the daytime; Correlation coefficient of (2013) Correlation coefficient of (2014)

11 JULES with Standard vs. Realistic Vegetation 1)Observed 4.4 km Vegetation (O-B) 2) Observed Realistic Vegetation (O-B) Surface Albedo (30 min observations) Emissivity (0.9 from 0.97) bare soil fraction (0.6 from 0.18) Vegetation canopy height (0.4 from 0.8 m) LAI (0.5 from 2.0) Blue = Night-time, Green = Transition period, Red = Daytime

12 Sensitivity of passive microwave emissivities to snowpack properties

13 AMSU-A MHS AVHRR IASI or AIRS MODIS Radar radiosondes Wind Slab synops Land Ocean

14 AMSU-A AMSR-E MHS AVHRR IASI or AIRS Laser Altimeter MODIS Radar Goal: Use all channels available to sound the temperature and humidity structure of the atmosphere. Perk: Gain a good deal of information about the surface in the process. Land Wind Slab Depth Hoar Sea Ice Ocean

15 Problem: Actual emissivities quite variable Emissivities from Harlow (2011) based on 24 surface types from 7 flights from CLPX-II (2008) and POLEX (2001) for Arctic Thin ice Emissivities of thin ice flat and near.9 or above. Emissivities of thicker ice and land types quite variable More so at 89 GHz than higher freq. Repetition of spectral shape related to surface conditions. G157-89

16 mm-wave emissivities effected by surface type Broad surface classes in alternating grey and white mm-wave emis can be related to surface type (Harlow, 2011) GHz emis difference sensitive to snow depth and statigraphy. 89 GHz radiation penetrates cm in pack. Penetration at 157 GHz shallower. Insensitive to ice surface roughness and salinity mm-waves sound the snowpack Increasing snow depth and depth hoar fraction

17 Need for detailed snow model Emissivities sensitive to grain size and packing of layers Layer density, thickness and wetness. Snow cover fractions. Need a snow model to evolve the snow pack with changing meteorological conditions. Coupled thermodynamic model such as snow module in JULES surface scheme with dynamic layering and a snow emission module to allow assimilation of surface sensing channels. Ideally would grow grains or evolve the porous snow medium with changes in meteorological conditions (cutting edge of science) Half-way house might be simple two layer model with depths of slab and hoar constrained by assimilation. Snow emission module needs to be validated for variety of snowpack conditions (Arctic conditions Harlow and Essery (2012))

18 Use for detailed snow module within atmospheric models Improve NWP model performance by allowing assimilation of full range of AMSU/MHS channels Improved retrieval of SWE and SD for hydrological and sea ice communities Improved knowledge of snowpack stratigraphy yields better thermal properties which is useful for seasonal and climate. Assimilation within NWP model Surface sensing channels sound the snow pack while the sounding channels sound the atmosphere. Requires a whole earth-systems approach

19 Summary There have been recent improvements in the biases in LST in subtropical semi-arid regions investigated during SALSTICE. Surface temperature and emissivity remain poorly monitored in the Unified Model particularly in the polar regions. Current MW and IR channels sensitive to surface and lower troposphere over land and sea ice are given extremely low weight (or blacklisted) within the NWP-DA system. However, representation of land and ice surface temperatures are extremely important for improvement of forecasts of important details such as near surface air temperature and snow/ice melting rates. The skin surface temperature is the lower boundary condition of the atmospheric temperature and the upper boundary of the snow pack.

20 Summary YOPP provides an opportunity to improve on the current situation JULES has a new multi-layer snow scheme YOPP international community hope to produce coupled atmosphere-ice-ocean models cm- to mm-wave MW signals have been shown to be sensitive to both the properties of the atmosphere and the properties of the underlying snowpack. YOPP observation and modelling efforts could close the loop on a coupled atmosphere-snow assimilation system. Such a system would reap forecasting benefits to those studying weather, climate and hydrology in arctic regions.

21 Questions and answers

22 Key instruments on FAAM Microwave Radiometers MARSS demonstrator for AMSU-B, MHS and MWS ISMAR ICI/MWI demonstrator Deimos AMSU-A demonstrator Several channels can be used to simultaneously retrieve temperature and emissivity IR Radiometers ARIES IR interferometer useful in retrievals of skin temperature and emissivity Heimann Radiometer increased spatial resolution but must be usec with ARIES as only down-looking.

23 Key instruments on FAAM (cont d) Broad-band radiometers (BBR s) Measure outgoing and incoming longwave and shortwave Essential for measurement of surface energy budget In situ instrumentation measuring air temperature, humidity, winds, turbulent fluxes, cloud and aerosol properties. All above are useful for improved understanding of the radiative properties of the atmosphere through which remote sensing measurements are being taken or for understanding the complete energy budget.

24 Overview of past Met Office radiation campaigns ICE-D July 2015, Cape Verde. Investigation of impact of dust optical properties on TIR retrieval of SST. COSMICS March 2015, concentrating on MW and sub-mm RT in ice cloud over UK and Iceland and surface emission and cal/val over Greenland SALSTICE May 2013, Arizona. LST retrievals and evaluation of JULES with aircraft and ground-based data. Cal/val of IASI-2 CAVIAR July 2009, Switzerland. Investigation of water vapour continuum absorption in VIS, NIR, TIR, and FIR. MEVEX - April 2009, Oman. Dusty IR radiative transfer studies. CLPX-II Feb. 2008, Alaska. Surface emission over snow-covered land and sea ice with collocated snow pits; validation of snow RT models. JAIVEX April 2007, Texas. Cal/val of IASI-1; IR surface emission POLEX March 2001, Norway and Svalbard. Surface emission over snowcovered land and sea ice

25 Microwave sounding for NWP AMSU-A weights AMSU-B weights AMSU-A and AMSU-B have channels which sense temperature and humidity in the UTLS Are sensitive to the surface mostly Are sensitive to the LT and the surface

26 Over open sea What AMSU does the Met Office currently assimilate? AMSU-A Ch. 1,2,4-14 AMSU-B Ch. 3-5 Over sea ice and land Only AMSU-A Ch No AMSU-B Information thrown away over sea ice and land pertaining to humidity and temperature of the lower atmosphere Surface temperature and surface properties.