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!"#$%&'()*+,-%&*%(.*+,"/.0* - PowerPoint PPT Presentation

!"#$%&'()*+,-%&*%(.*+,"/.0* &%.'%(+-1*'(*23-*1%#-*1+-(-* 4',,*5&'"(* 6-2*7&"$/,1'"(*8%9"&%2"&0* !%,':"&('%*5(1;2/2-*":*<-+3(",")0*


  1. !"#$%&'()*+,-%&*%(.*+,"/.0* &%.'%(+-1*'(*23-*1%#-*1+-(-* 4',,*5&'"(* 6-2*7&"$/,1'"(*8%9"&%2"&0* !%,':"&('%*5(1;2/2-*":*<-+3(",")0*

  2. =">*>-,,*."*+,"/.?+,-%&-.*%(.*+,"/.? :&--*&%.'%(+-1*%)&--@* '$(#&)#*+,!$*#&*!#-$%./*&0&12$"32%4&5&67&12$"889$:&5&6& AB*C'(.* !"#$%& 8A4*D5EF*:""2$&'(2* '(1'.-*DGFH*:""2$&'(2* I(')3;#-J*"+-%(*"(,0* I/1-*K-"&)-*D/#%((L1*%(.*4&'%(* M%3(L1*#-23".1*N !"#$%&'%"() J*OPPQR* :"&*+,"/.*1+&--('()* I/1-*DGFE?S*<"2%,*T%2-&*:"&*4=M* 2-12* <%U-*+,"/.?:&--*8A4*4<1*%(.* +"'(+'.-(2*+,"/.?+,-%&-.*8O*4<1*

  3. OB*F/92&%+2*+,"/.?:&--*8A4*4<*:&"#*+"'(+'.-(2*+,"/.?+,-%&-.*8O*4<* VB*W"*23'1*:"&*AQ 23 *":*-%+3*#"(23*:"&*F-$2*AQ*:&"#*XYPO*2"*VYAPZ* [B*K-2*1,"$-*":* � 4<*:"&*-%+3*+3%((-,*90*,'(-%&*&-)&-11'"(* \-]+,/.'()*+3%((-,1*)"(-*9%.ZB* Fitted line, not zero line …and data can be binned by latitude, season, cloud pressure, etc. This is a preliminary study, and results are sensitive to clear selection criteria.

  4. K,"9%,*&-1/,21* D,,*,%21J*1-%1"(1J* BIAS +,"/.1*'(*!!*1$-+2&%* 4'((-.*90*+,-%&*2-12* $&"+-./&-* 4'%1*%(.*1,"$-1*1#%,,J* 9/2*+%,+/,%;"(1*%&-* 1-(1';^-*2"*+,-%&*2-12* +&'2-&'%* 4'))-12*-_-+2*'1* SLOPE #"12,0*"(*>'(.">* +3%((-,1Z**

  5. K,"9%,*&-1/,21* BIAS 9'((-.*'(2"*1-%1"(* D,,*,%21J*+,"/.1*'(*!!* 1$-+2&%* D/#%((*%(.*M%3(* 2-121*%$$,'-.* 4'%1*%(.*1,"$-*^%&0*90* SLOPE 1-%1"(*

  6. K,"9%,*&-1/,21* BIAS 9'((-.*'(2"*+,"/.?2"$* $&-11/&-* D,,*,%21J*1-%1"(1*'(*!!* 1$-+2&%* D/#%((*%(.*M%3(* SLOPE 2-121*%$$,'-.* 4'%1*%(.*1,"$-*^%&0*90* +,"/.?2"$*$&-11/&-*

  7. K,"9%,*&-1/,21* BIAS 9'((-.*'(2"*+,"/.?2"$* $&-11/&-* D,,*,%21J*1-%1"(1*'(*!!* 1$-+2&%* Error bar on slope D/#%((*%(.*M%3(* SLOPE 2-121*%$$,'-.* 4'%1*%(.*1,"$-*^%&0*90* +,"/.?2"$*$&-11/&-*

  8. <3'1*'1*%*$&-,'#'(%&0*12/.0 * Biases and slopes are small, but are highest on window channels. In time, higher-valued slopes may add significant bias? Calculation of biases and slopes strongly dependent on criteria for finding a clear AIRS footprint. This is likely not a large source of the temperature bias trends (and is often of opposite sign). BUT These are observations where cloud-clearing should be expected to work extremely well, and so this can be useful as a sanity check on calculation of cloud-cleared radiances under different conditions.

  9. Thank you. No, this is not one of my dogs.

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