Iredell, Lena

Molnar, Gyula

Reale, Oreste

Rosenberg, Robert

Susskind, Joel

NASA Technical Reports Server

Recent Advances in Forecast Skill and Understanding 
Climate Process Using AIRS Version-5 Products 
 
Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg 
and Oreste Reale 
NASA GSFC 
Earth Sciences Division - Atmospheres, Code 610 
 
 
SPIE Optics & Photonics 2012 
Infrared and Remote Sensing and Instrumentation XX 
Paper 8511-25 
August 14, 2012 
San Diego, California 
 
 
National Aeronautics and Space Administration 
www.nasa.gov  
https://ntrs.nasa.gov/search.jsp?R=20120015050 2019-08-30T22:23:48+00:00Z
National Aeronautics and Space Administration   Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 2 
AIRS/AMSU on Aqua 
AIRS/AMSU launched on EOS Aqua May 5, 2002 
AIRS is a multi-detector array grating spectrometer 
2378 channels between 650 cm-1 and 2760 cm-1 
Channel spacing ≈ ν/2400 (0.25 cm-1 – 1.1 cm-1) 
Resolving power ν/Δν ≈ 1200 (0.5 cm-1 – 2.2 cm-1) 
AIRS Field of View (FOV) 13 km x 13 km at nadir 
AMSU is a microwave sounder with a nadir 45 km x 45 km Field of 
Regard (FOR) 
  AMSU-A has flown on previous satellites 
3 x 3 AIRS FOV’s within an AMSU-A FOR 
One AIRS/AMSU sounding is produced per AMSU-A FOR 
 
 
National Aeronautics and Space Administration 
 
 
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 3 
Overview of AIRS/AMSU Retrieval Methodology 
Physically based retrieval system 
Independent of GCM except for surface pressure - used to compute 
expected radiances 
Uses cloud cleared radiances Ri to sequentially determine the solution for 
different geophysical parameters 
   Ri represents what AIRS would have seen in the absence of clouds  
All AIRS/AMSU retrievals have error estimates which are used for Quality 
Control 
Goddard DISC has been analyzing AIRS/AMSU data using AIRS Version-5 
algorithm 
 Retrievals are near real time 
 Analyzed data from September 2002 through the present 
AIRS Version-6 algorithm is finalized – see paper 8510-29 Wed. at 11:20 a.m. 
AIRS Version-6 will become operational in late 2012 for forward processing 
 All previous data will be re-analyzed to produce a consistent climate 
 data set 
 
 
National Aeronautics and Space Administration   Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 4 
AIRS/AMSU Products 
Surface and Atmospheric Products – one set per AMSU FOR (45 km) 
 Sea/land skin temperature Ts 
 Temperature profile T(p) to 1 mb 
 Water vapor profile q(p) to 100 mb 
 O3, CO, CH4,CO2 profiles 
 Cloud Cleared Radiances Ri 
Cloud Products – one set per AIRS FOV (13 km). 
 Radiatively effective cloud fraction αε for up to two cloud layers 
  α is geometric fractional cloud cover 
  ε is cloud emissivity at 11 µm 
 Cloud top pressure pc for up to two cloud layers. 
 
Outgoing Longwave Radiation (OLR) is computed using αε, pc, and 
retrieved parameters. 
 
Cloud clearing theory says that 
 1) Longwave channels should be used to determine Ri for all channels. 
 2) T(p) and Ts should be determined using Ri only in shortwave  
 channels and longwave channels not sensitive to clouds. 
  
National Aeronautics and Space Administration 
     
 Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 5 
National Aeronautics and Space Administration 
     
 Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 6 
Objectives of AIRS/AMSU 
• Provide real time observations to improve numerical weather prediction. 
 Could be Ri (used by NCEP, ECMWF) or T(p), q(p). 
 Accuracy of Ri T(p), q(p) degrades slowly with increasing cloud fraction. 
 There is a trade-off between accuracy and spatial coverage. 
       Using soundings or radiances only in clear cases limits utility of the  
 data. 
 
• Provide observations to measure and explain interannual variability and 
 trends. 
  Must provide very good spatial coverage but also be unbiased 
  Can be less accurate than needed for data assimilation.  
 
• Error estimates and quality flags provide options for use in either 
 weather or climate applications. 
 
 
National Aeronautics and Space Administration 
    
  
                                  
                             
            Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25   7 
AIRS/AMSU Version-5 Quality Control 
All AIRS/AMSU retrievals and cloud cleared radiances have level-by-level and 
channel-by-channels error estimates. Thresholds of error estimates are 
used for Quality Control (QC) of all retrievals – determines pressure pbest 
down to which T(p) is acceptable. 
 
Version-5 used Standard QC Thresholds, optimized to give a best middle 
ground for Data Assimilation (highest accuracy) and Climate (best spatial 
coverage). Tighter QC thresholds perform better for Data Assimilation (DA). 
 
Much outstanding science has been conducted using AIRS Version-5 
products. 
 
I will show two studies related to:  1) improving forecast skill and  
 2) explaining why global and tropical mean OLR has decreased recently. 
 
Version-6 will have further improvements, including separate sets of 
thresholds optimal for weather and climate applications and an  
 “AIRS Only” (AO) mode to serve as a back-up should AMSU-A continue to 
degrade. 
  
 
National Aeronautics and Space Administration     Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  8 
     Global Temperature Profile Statistics 
                   
  Percent of All Cases                         1 km Layer Mean RMS (°K)                        1 km Layer Mean BIAS (°K) 
                            Accepted                                        Differences from ECMWF                          Differences from ECMWF 
Version-6 T(p) retrievals with DA QC have RMS errors ≤ 1K throughout troposphere 
Version-6 T(p) retrievals with Climate QC have much greater yield than Verison-5 with small biases 
Differences between Version-6 and Version-6 AO are small   
National Aeronautics and Space Administration                                 Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 9 
Data Assimilation Experiments 
Ran experiments with GMAO GEOS-5 Data Assimilation System (DAS). 
     •  Uses NCEP Operational GSI Data Assimilation Methodology.  
     •  Forecasts and NCEP GSI Analysis run at 0.5° x 0.625° resolution. 
We compared forecasts from four assimilations: 
 
 
 
 
 
Control used all the data NCEP used operationally in 2003. 
    • Assimilates all satellite data but AIRS, including Aqua AMSU radiances 
 
AIRS Radiance Assimilation (Operational procedure) adds observed AIRS 
radiances unaffected by clouds to Control – excludes most AIRS observations. 
 
Control + AIRS (Standard or Tight) adds Version-5 global Quality Controlled 
temperature profile retrievals to Control. 
    • Assimilated as if radiosonde data. 
    • Error estimate of temperature profile is used as the level measurement error.   
 
 
Control No AIRS 
AIRS V5 temperature profile, Standard QC 
AIRS V5 temperature profile, Tight QC 
AIRS radiance assimilation (Operational) 
National Aeronautics and Space Administration  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  10 
 
Forecast Impact Tests 
Ran four sets of experiments, covering different seasons and years. 
     January 1 – January 31, 2003 
     October 15 – November 19, 2005 
     August 10 – September 16, 2006 
     April 15 – May 18, 2008 (Tropical Storm Nargis) 
 
The AIRS temperature profiles are assimilated down to pbest determined 
using appropriate QC thresholds. 
 
Seven day Forecasts run from each 0 Z Analysis for each experiment. 
 
The accuracy is judged against anomaly correlation of 7-day forecasts 
vs. ECMWF Analysis for that time. 
 
 
  
 
National Aeronautics and Space Administration   Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  11 
An anomaly correlation of 1.0 represents a perfect forecast 
An anomaly correlation of 0.6 is the lower bound of a useful forecast 
AIRS Tight improves seven day forecast skill by about five hours compared to No AIRS  
GEOS-5 Control (No AIRS) 
GEOS-5 AIRS Standard 
GEOS-5 AIRS Tight 
GEOS-5 Radiance (Operational) 
500mb Geopotential Heights 
Northern Hemisphere Extra Tropics 
 
An
om
al
y 
 C
or
re
la
tio
n 
    Day 
 
Average of Summer 2006, Spring 2008, Fall 
2005, and Winter 2003 
National Aeronautics and Space Administration   Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  12 
Example of AIRS Impact on Forecast Track 
Tropical storm Nargis 
 
AIRS 
Analysis 
Well-defined 
Cyclone 
Green: 
Observed  
Track 
AIRS 108-hour 
Forecast (slp) 
 
Green: 
Observed  
Track 
CNTRL Analysis (above) 
And forecast (below):  No Cyclone 
Accurate landfall is produced in the forecasts initialized with 
AIRS: (Reale et al., 2009, Geophys. Res. Lett.) 
National Aeronautics and Space Administration  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 13 
Reale, O., K. M. Lau, J. Susskind, and R. Rosenberg (2012), AIRS impact on 
analysis and forecast of an extreme rainfall event (Indus River Valley, 
Pakistan, 2010) with a global data assimilation and forecast system,
J. Geophys. Res., 117,D08103, doi:10.1029/2011JD017093.
OBS (TRMM)
GEOS-5 Forecast using v5 Retrievals (RET)
RET minus RAD
Assimilation of AIRSv5 retrievals goes in the direction of the observations
GEOS-5 Forecast using v5 Clear-sky radiances (RAD)
7-day accumulated precipitation forecast initialized at July 22, 2010 00z
National Aeronautics and Space Administration    Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  14 
 
First Experiment Using NCEP GFS 
Lou Uccellini, Director of NCEP, was very impressed with the potential of 
this methodology to improve operational forecast skill. 
 New methodology must be first tested with the GFS system used 
operationally at NCEP. 
 
        
Lou Uccellini, together with Tsengdar Lee (HQ) arranged to have the NCEP 
GFS ported to GSFC. 
 
We have just started to run the first experiment assimilating AIRS  
 Version-5 T(p) in GFS. 
Covers the period July-August 2010, containing Pakistan flood. 
Results are encouraging but too preliminary to be shown at this time. 
 
Future experiments will contain 
 • Tests with other time periods. 
 • Optimization of DA QC thresholds for use in GFS. 
 • Experiments assimilating Version-6 T(p) retrievals.  
  
 
National Aeronautics and Space Administration 
• Comparison of AIRS and CERES anomaly time series of OLR. 
• Explanation of recent decreases in global and tropical mean values of 
OLR. 
Data sets used 
AIRS Science Team Version-5 monthly mean data obtained from 
Goddard DISC (level-3). 
 OLR, Tskin, q500, cloud fraction αε. 
 Data products extend to July 2012. 
CERES Science Team monthly mean data obtained from Langley ASDC 
 CERES Terra EBAF (Energy Balanced And Filled) Edition 2.6r. 
 Data products extend to June 2011. 
 CERES is considered the “gold standard” for OLR 
AIRS/CERES comparisons are shown for the period September 2002 
through June 2011. 
Example of Climate Research 
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  15 
National Aeronautics and Space Administration 
Eight-year monthly climatologies were generated for each 1˚x 1˚ grid box 
by averaging data for eight Januaries, eight Februaries, …… 
The monthly anomaly for each grid box is the difference of the monthly 
mean value for that month from its climatology. 
The Average Rate of Change (ARC) for a grid box is the slope of the 
straight line passing through the monthly anomaly time series. 
  Values of ARCs depend on the extent of the time series used. 
  Spatial patterns are more important than precise values. 
An area mean ARC is the cosine latitude weighted average ARC over the 
area. 
The El Niño Correlation (ENC) for a given grid point is the temporal 
correlation of the anomaly time series for that grid point with the         
El Niño Index. ENCs should be less time period dependent. 
 
 
    
Definition of Anomalies, ARCs and ENCs 
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  16 
National Aeronautics and Space Administration 
AIRS   
CERES   
AIRS minus CERES   
 
AIRS  
CERES 
AIRS minus CERES 
1.5 times the El Niño Index 
El Niño Index ARC = -0.123 ± 0.046 K/yr 
OLR Anomaly Time Series 
September 2002 through June 2011 
 
                      Global Mean                                Tropical Mean 
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  17 
National Aeronautics and Space Administration 
OLR Anomaly Time Series Comparison 
September 2002 through June 2011 
 
Data Set Global Tropical 
AIRS ARC (W/m2/yr) −0.094 ± 0.026  −0.183 ± 0.070 
CERES Terra ARC (W/m2/yr) −0.059 ± 0.022  −0.154 ± 0.066 
AIRS Minus CERES STD (W/m2) 0.136 0.155 
AIRS/CERES Correlation 0.955 0.991 
AIRS ENC 0.587 0.830 
CERES ENC 0.523 0.813 
AIRS and CERES global mean and tropical mean anomaly time series agree very closely 
with each other. 
Both show that global, and especially tropical, mean OLR have decreased over the time 
period under study. 
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  18 
National Aeronautics and Space Administration 
Important Highlighted Regions in Subsequent Figures 
 
 
NOAA Niño-4 Region:    
    We define the El Niño Index as the  
NOAA monthly  mean Tskin anomaly,  
compared to 8-year climatology, averaged 
over the NOAA Niño-4 region 
5°N to 5°S, 150°W, westward to 160°E 
 
 
 
 
 
Warm Pool Maritime Continent (WPMC) region:        
West of Niño-4 region 
 
Equatorial Eastern Pacific and Atlantic (EEPA) region:     
East of Niño-4 region 
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  19 
National Aeronautics and Space Administration   Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25  20 
b) 
ARCs and ENCs of OLR Anomaly Time Series 
September 2002 through June 2011 
       Global Mean=-0.059   STD=0.82                   Global Mean=-0.094   STD= 0.82 
 a)                                             AIRS  ARCs                                           b)                        CERES ARCs  
OLR ARCs are very negative over the WPMC and EEPA regions (blue, yellow). 
OLR ENCs are very positive over the WPMC and EEPA regions (blue, yellow). 
 a)                                           AIRS  ENCs                                          b)                            CERES ENCs  
           Global Mean=0.04       STD=0.33                Global Mean=0.03       STD=0.36 
National Aeronautics and Space Administration  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale  – SPIE Paper #8511-25  21 
 
Spatial Area 
 
OLR ARC  
(W/m2/yr) 
 
OLR ENC 
 
OLR ARC  
(W/m2/yr) 
 
OLR ENC 
WPMC Region -1.502 ± 0.525 0.871 -1.496 ± 0.529 0.870 
Tropical outside 
WPMC Region 0.004 ± 0.054 0.050 0.034 ± 0.053 0.101 
Global outside  
WPMC Region -0.030 ± 0.028 -0.129 0.005 ± 0.027 -0.372 
EEPA Region -0.631 ± 0.158 0.767 -0.611 ± 0.154 0.761 
Tropical outside  
EEPA Region -0.037 ± 0.048 0.599 -0.011 ± 0.047 0.511 
Global outside  
EEPA Region -0.044 ± 0.020 0.256 -0.011 ± 0.019 0.039 
      
Area Mean Statistics for AIRS and CERES OLR 
              September 2002 through June 2011 
AIRS CERES 
ARCs of otherwise Tropical mean and Global mean OLR computed 
outside of either WPMC region or EEPA region are essentially zero. 
National Aeronautics and Space Administration                       Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste  Reale – SPIE Paper #8511-25  22 
Tskin ARCs and ENCs are for the most part of opposite sign (similar color). This shows most ARCs 
are El Niño driven, except near the North Pole. 
ENCs of q500 and αε are similar to each other and are both very negative over the WPMC and 
EEPA regions. This results in positive OLR ENC’s in these regions. 
     Global Mean = -0.08    STD = 0.35 
 c)                500 mb Specific Humidity ENCs                                   d)                 Effective Cloud Fraction ENCs 
               
                               AIRS Products September 2002 through June 2011 
  a)                            Tskin ARCs (K/yr)                                           b)                              Tskin ENCs  
                                                                                  
 
Global Mean = -0.01    STD = 0.10 
                        Global Mean=0.06        STD=0.34                                Global Mean=-0.08      STD=0.28 
National Aeronautics and Space Administration 
Findings of Climate Study 
Recent AIRS and CERES OLR anomaly time series are in  very close 
agreement on a 1° spatial scale. 
Both show a significant decrease in global mean and tropical mean OLR 
over the period September 2002 through June 2011. 
The degree of agreement is remarkable, but the recent OLR decrease is not 
because it is the result of a transition from El Niño conditions to La Niña 
conditions. This implies nothing about long term changes. 
The recent decrease in global and tropical mean OLR results from the very 
large negative correlations of water vapor and cloud cover in each of the 
WPMC and EEPA regions with the El Niño Index. This more than 
compensates for the reverse effect in the Niño-4 region. 
   
AIRS is expected to last through the life of Aqua ≈2022. 
 
This will allow for a 20 year AIRS Climate Data Set. 
 
Version-6 climate products will be further improved over Version-5. 
   
  Joel Susskind, Gyula Molnar, Lena Iredell, Robert Rosenberg , Oreste Reale – SPIE Paper #8511-25 23 
National Aeronautics and Space Administration   Joel Susskind 24 


Recent Advances in Forecast Skill and Understanding Climate Process Using AIRS Version-5 Products

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server