Re: [Qpack] jacobian non-retrieval parameters
Hi Patrick, Thanks a lot. I am trying to retrieve ozone profiles considering the covariance matrix of temperature and water vapour. With 'good shape' I mean that it is around a number in the whole frequency range (straight). Now I included a calculation of a baseline in each iteration and it seems to improve the residual. I will also try to set POLYFIT in Q.DEFINITIONS.m. Thanks again. Regards Facundo 2015-12-06 10:05 GMT-03:00 Patrick Eriksson : > Hi Facundo, > > I am writing again because I did that I said in the previous mail and >> retrieved ozone profiles are not bad, but the residual (yf-y) is not >> around zero...It goes down to around 0.3 (the shape is good, but goes >> down). It happens when I introduce the error related to T and H2O, and >> also when I introduce only T or only H2O. Residual are very similar in >> this 3 cases (T and H2O, T or H2O). When I do not include these errors >> (only considering noise) it does not happen. I was looking for what is >> wrong and I could not find it. Do you have any suggestion about it? >> > > This is not easy to answer without having all the details at hand. Exactly > how you calibrate the measurements, exact retrieval settings etc. Some > questions: > > * What do you mean with good shape of the residual? > > * Do you make a non-linear retrieval? With T or H2O you might need to > iterate to find the exact solution. > > * Do you include any POLYFIT? To include POLYFIT is quite standard, to > reflect that your calibration newer is perfect. It is likely that the > complete spectrum is wrong with roughly 1K just due to calibration issues. > > > On the other hand, what is exactly the use of the variable 'xnorm'? >> > > It is a feature to avoid numerical problems if your quantities have very > different values. If you use Qpack, this is activated automatically, when > could be needed. > > Bye, > > Patrick > ___ qpack mailing list [email protected] https://www.sat.ltu.se/mailman/listinfo/qpack
Re: [Qpack] jacobian non-retrieval parameters
Hi Facundo, > I am writing again because I did that I said in the previous mail and > retrieved ozone profiles are not bad, but the residual (yf-y) is not > around zero...It goes down to around 0.3 (the shape is good, but goes > down). It happens when I introduce the error related to T and H2O, and > also when I introduce only T or only H2O. Residual are very similar in > this 3 cases (T and H2O, T or H2O). When I do not include these errors > (only considering noise) it does not happen. I was looking for what is > wrong and I could not find it. Do you have any suggestion about it? This is not easy to answer without having all the details at hand. Exactly how you calibrate the measurements, exact retrieval settings etc. Some questions: * What do you mean with good shape of the residual? * Do you make a non-linear retrieval? With T or H2O you might need to iterate to find the exact solution. * Do you include any POLYFIT? To include POLYFIT is quite standard, to reflect that your calibration newer is perfect. It is likely that the complete spectrum is wrong with roughly 1K just due to calibration issues. > On the other hand, what is exactly the use of the variable 'xnorm'? It is a feature to avoid numerical problems if your quantities have very different values. If you use Qpack, this is activated automatically, when could be needed. Bye, Patrick ___ qpack mailing list [email protected] https://www.sat.ltu.se/mailman/listinfo/qpack
Re: [Qpack] jacobian non-retrieval parameters
Hi Patrick, Thanks a lot for the information provided. I am writing again because I did that I said in the previous mail and retrieved ozone profiles are not bad, but the residual (yf-y) is not around zero...It goes down to around 0.3 (the shape is good, but goes down). It happens when I introduce the error related to T and H2O, and also when I introduce only T or only H2O. Residual are very similar in this 3 cases (T and H2O, T or H2O). When I do not include these errors (only considering noise) it does not happen. I was looking for what is wrong and I could not find it. Do you have any suggestion about it? On the other hand, what is exactly the use of the variable 'xnorm'? Thanks a lot Best Regards Facundo 2015-11-12 5:05 GMT-03:00 Patrick Eriksson : > Hi Facundo, > > Yes, totally correct. This is exactly what should be done if you treat H2O > and temperature as forward model parameter uncertainties. That this is OK > (and better than setting Se=Sn) is not clearly expressed by Rodgers, and as > a reference for this I append one of my first articles: > > https://dl.dropboxusercontent.com/u/102809064/eriksson-2000-analy.pdf > > See Eq 8. Yes, you are right, the impact of H2O and temperature will > automatically be part of the retrieval error (e.g. in L2.speciesX_eo). > > > Let me just make another thing clear. As I have written, you obtain the > same result by instead retrieving H2O and temperature in parallel to ozone. > For a linear case, you will get identical results. And this is the way I > think most people are handling interfering effects today. However, it is > here important to note that with this later set-up, the impact of H2O and > temperature comes out as part of the smoothing error (l2.speciesX_es). > Accidentally, I am writing a document right now where I felt forced to dig > into this and give a detailed explanation. So maybe I will come back to > this point later, when I have something that can be circulated. > > Bye, > > Patrick > > > > > > On 11/11/15 22:01, Facundo Orte wrote: > >> Patrick, >> thank you very much for your reply. >> >> I will proceed to share what I did. I implemented the first of your >> suggestions. So, >> >> Se = Sn + Kt*St*Kt^T + Kh2o*Sh2o*Kh2o^T >> >> (I changed the nomenclature with respect to the previous email to not >> confuse with the nomenclature of Matlab functions) >> >> where: >> Sn: covariance related with noise (in Matlab function, >> Sn=Y.TNOISE^2.*Q.TNOISE_C) >> Kt: jacobian temperature >> St: covariance matrix of temperature >> Kh2o: jacobian of water vapour >> Sh2o: covariance matrix of water vapour >> >> I implemented it inside of a function called qp2_y2Q, which is inside of >> qpack2.m. qp2_y2Q is implemented as: >> >> [Q,Se] = qp2_y2Q( Q, Y, m); >> >> where the output Se is now the quantity in the equation of the >> covariance matrix as above (Se = Sn + Kt*St*Kt^T + Kh2o*Sh2o*Kh2o^T). >> Therefore, the retrieval error will consider the error due to noise, >> temperature and H2O without any other modification in Qpack because the >> calculation of other retrieval parameters (such as cost, G, etc.) in >> oem.m takes into account the covariance matrix Se. >> >> Is it correct? >> >> Thanks in advance >> >> Best regards >> >> Facundo >> >> >> >> >> 2015-11-01 18:17 GMT-03:00 Patrick Eriksson >> mailto:[email protected]>>: >> >> >> Facundo, >> >> You were not unclear, I tried to answer exactly what you wrote in >> your last email. >> >> First of all, if you want to sum up uncertainties you should not use >> matrix inverses. You do it as: >> >> S = Se + Kb*Sb*Kb^T >> >> >> (What you want to do was better supported in Qpack1, but I had a >> hard time to find a general way to cover all possible combinations >> that could be of interest. For Qpack2 you need to do some tricks.) >> >> Let us use T as example. If you want to get out the Kb matching T, >> you can e.g. deactivate all present retrieval variables, and just >> set T as retrieval variable, and Q.T.L2 = true. Then make a linear >> inversion and Kb = L2.J. (You can do the same thing for H2O. The >> uncertainty associated with O2 should be negligible.) >> >> With this Kb, the retrieval error due to T is calculated as >> >> Sr = G*Kb*St*Kb^T*G^T >> >> where St is the temperature covariance matrix. >> >> The above should answer your direct question. However, if the error >> due to T is significant, you can do better. This simple by >> retrieving T in parallel to O3. The extra calculation cost is quite >> small, and I would recommend you to do this as the error related to >> T will then be smaller. If you don't care about the result for T, >> you just set Q.T.L2=false. >> >> If you still want to calculate the error due to T, you can do as >> above, or use L2.jq and L2.ji to extract the part of L2.J that >> matches T, as I tried to describe in my last answer. >> >> If H2O should also b
Re: [Qpack] jacobian non-retrieval parameters
Hi Facundo, Yes, totally correct. This is exactly what should be done if you treat H2O and temperature as forward model parameter uncertainties. That this is OK (and better than setting Se=Sn) is not clearly expressed by Rodgers, and as a reference for this I append one of my first articles: https://dl.dropboxusercontent.com/u/102809064/eriksson-2000-analy.pdf See Eq 8. Yes, you are right, the impact of H2O and temperature will automatically be part of the retrieval error (e.g. in L2.speciesX_eo). Let me just make another thing clear. As I have written, you obtain the same result by instead retrieving H2O and temperature in parallel to ozone. For a linear case, you will get identical results. And this is the way I think most people are handling interfering effects today. However, it is here important to note that with this later set-up, the impact of H2O and temperature comes out as part of the smoothing error (l2.speciesX_es). Accidentally, I am writing a document right now where I felt forced to dig into this and give a detailed explanation. So maybe I will come back to this point later, when I have something that can be circulated. Bye, Patrick On 11/11/15 22:01, Facundo Orte wrote: > Patrick, > thank you very much for your reply. > > I will proceed to share what I did. I implemented the first of your > suggestions. So, > > Se = Sn + Kt*St*Kt^T + Kh2o*Sh2o*Kh2o^T > > (I changed the nomenclature with respect to the previous email to not > confuse with the nomenclature of Matlab functions) > > where: > Sn: covariance related with noise (in Matlab function, > Sn=Y.TNOISE^2.*Q.TNOISE_C) > Kt: jacobian temperature > St: covariance matrix of temperature > Kh2o: jacobian of water vapour > Sh2o: covariance matrix of water vapour > > I implemented it inside of a function called qp2_y2Q, which is inside of > qpack2.m. qp2_y2Q is implemented as: > > [Q,Se] = qp2_y2Q( Q, Y, m); > > where the output Se is now the quantity in the equation of the > covariance matrix as above (Se = Sn + Kt*St*Kt^T + Kh2o*Sh2o*Kh2o^T). > Therefore, the retrieval error will consider the error due to noise, > temperature and H2O without any other modification in Qpack because the > calculation of other retrieval parameters (such as cost, G, etc.) in > oem.m takes into account the covariance matrix Se. > > Is it correct? > > Thanks in advance > > Best regards > > Facundo > > > > > 2015-11-01 18:17 GMT-03:00 Patrick Eriksson > mailto:[email protected]>>: > > Facundo, > > You were not unclear, I tried to answer exactly what you wrote in > your last email. > > First of all, if you want to sum up uncertainties you should not use > matrix inverses. You do it as: > > S = Se + Kb*Sb*Kb^T > > > (What you want to do was better supported in Qpack1, but I had a > hard time to find a general way to cover all possible combinations > that could be of interest. For Qpack2 you need to do some tricks.) > > Let us use T as example. If you want to get out the Kb matching T, > you can e.g. deactivate all present retrieval variables, and just > set T as retrieval variable, and Q.T.L2 = true. Then make a linear > inversion and Kb = L2.J. (You can do the same thing for H2O. The > uncertainty associated with O2 should be negligible.) > > With this Kb, the retrieval error due to T is calculated as > > Sr = G*Kb*St*Kb^T*G^T > > where St is the temperature covariance matrix. > > The above should answer your direct question. However, if the error > due to T is significant, you can do better. This simple by > retrieving T in parallel to O3. The extra calculation cost is quite > small, and I would recommend you to do this as the error related to > T will then be smaller. If you don't care about the result for T, > you just set Q.T.L2=false. > > If you still want to calculate the error due to T, you can do as > above, or use L2.jq and L2.ji to extract the part of L2.J that > matches T, as I tried to describe in my last answer. > > If H2O should also be retrieved depends on your exact set-up. > > Bye, > > Patrick > > > > > > > On 10/29/15 14:19, Facundo Orte wrote: > > Dear Patrick, > Thank you so much for the reply and I am sorry for not be clear. > I set Qpack to retrieve only ozone. Other gases and T (H2O, O2 > and T) I > set as Q.ABS_SPECIES.RETRIEVE=false, because I am not interesting in > water vapor, oxygen, and T, but I include NCEP profiles of > these gases and temperature in Q.DEFINITIONS.m for ARTS > calculation and > to model the spectrum. O2 is included because the frequency range of > measurement is 110.83GHz and this gas also absorb the signal. These > profiles have some uncertainties and I want to introduce these > uncertainties in the covariance matrix for the cost calculation > and so > on.
Re: [Qpack] jacobian non-retrieval parameters
Patrick, thank you very much for your reply. I will proceed to share what I did. I implemented the first of your suggestions. So, Se = Sn + Kt*St*Kt^T + Kh2o*Sh2o*Kh2o^T (I changed the nomenclature with respect to the previous email to not confuse with the nomenclature of Matlab functions) where: Sn: covariance related with noise (in Matlab function, Sn=Y.TNOISE^2.*Q.TNOISE_C) Kt: jacobian temperature St: covariance matrix of temperature Kh2o: jacobian of water vapour Sh2o: covariance matrix of water vapour I implemented it inside of a function called qp2_y2Q, which is inside of qpack2.m. qp2_y2Q is implemented as: [Q,Se] = qp2_y2Q( Q, Y, m); where the output Se is now the quantity in the equation of the covariance matrix as above (Se = Sn + Kt*St*Kt^T + Kh2o*Sh2o*Kh2o^T). Therefore, the retrieval error will consider the error due to noise, temperature and H2O without any other modification in Qpack because the calculation of other retrieval parameters (such as cost, G, etc.) in oem.m takes into account the covariance matrix Se. Is it correct? Thanks in advance Best regards Facundo 2015-11-01 18:17 GMT-03:00 Patrick Eriksson : > Facundo, > > You were not unclear, I tried to answer exactly what you wrote in your > last email. > > First of all, if you want to sum up uncertainties you should not use > matrix inverses. You do it as: > > S = Se + Kb*Sb*Kb^T > > > (What you want to do was better supported in Qpack1, but I had a hard time > to find a general way to cover all possible combinations that could be of > interest. For Qpack2 you need to do some tricks.) > > Let us use T as example. If you want to get out the Kb matching T, you can > e.g. deactivate all present retrieval variables, and just set T as > retrieval variable, and Q.T.L2 = true. Then make a linear inversion and Kb > = L2.J. (You can do the same thing for H2O. The uncertainty associated with > O2 should be negligible.) > > With this Kb, the retrieval error due to T is calculated as > > Sr = G*Kb*St*Kb^T*G^T > > where St is the temperature covariance matrix. > > The above should answer your direct question. However, if the error due to > T is significant, you can do better. This simple by retrieving T in > parallel to O3. The extra calculation cost is quite small, and I would > recommend you to do this as the error related to T will then be smaller. If > you don't care about the result for T, you just set Q.T.L2=false. > > If you still want to calculate the error due to T, you can do as above, or > use L2.jq and L2.ji to extract the part of L2.J that matches T, as I tried > to describe in my last answer. > > If H2O should also be retrieved depends on your exact set-up. > > Bye, > > Patrick > > > > > > > On 10/29/15 14:19, Facundo Orte wrote: > >> Dear Patrick, >> Thank you so much for the reply and I am sorry for not be clear. >> I set Qpack to retrieve only ozone. Other gases and T (H2O, O2 and T) I >> set as Q.ABS_SPECIES.RETRIEVE=false, because I am not interesting in >> water vapor, oxygen, and T, but I include NCEP profiles of >> these gases and temperature in Q.DEFINITIONS.m for ARTS calculation and >> to model the spectrum. O2 is included because the frequency range of >> measurement is 110.83GHz and this gas also absorb the signal. These >> profiles have some uncertainties and I want to introduce these >> uncertainties in the covariance matrix for the cost calculation and so >> on. What I need is to include these uncertainties in the covariance >> matrix. >> >> I think that I can include it in the covariance matrix doing: >> >> S^(-1)=Se^(-1) + Kb^T*Sb^(-1)*Kb >> >> where Kb is the jacobian of non-retrieval parameters (in my case H2O, T, >> and O2), Sb is the covariance matrix related with the non-retrieval >> parameters and Se is the covariance matrix related with noise. S is the >> covariance matrix used to calculate the cost as following. >> >> cost=[y-F(x,b)]^T * *S*^(-1) * [y-F(x,b)] + [x-xa]^T * Sx^(-1) * [x-xa] >> >> At this time, I introduce the covariance matrix only related with noise >> (S=Se) and I not include the uncertainties for water vapor, T and O2 >> profiles. >> >> I think that the Jacobians of non-retrieval parameters are similar for >> different measurements. So, my idea is to calculate these jacobians one >> time and use it then for the calculation of different measurements. It >> will avoid the calculation of these jacobians in each measurement. >> >> My problem is that I do not know how to include the uncertainties for >> non-retrieval parameters profiles in the covariance matrix S. >> >> Thanks in advance >> Best regards >> >> 2015-10-27 16:55 GMT-03:00 Patrick Eriksson >> mailto:[email protected]>>: >> >> >> Dear Facundo, >> >> As I understand your question, the answer is no. Or at least, this >> can not be done automatically. >> >> However, if you think that H2O and temperature can give substantial >> errors (I can not see how O2 should give rise to an error), y
Re: [Qpack] jacobian non-retrieval parameters
Facundo, You were not unclear, I tried to answer exactly what you wrote in your last email. First of all, if you want to sum up uncertainties you should not use matrix inverses. You do it as: S = Se + Kb*Sb*Kb^T (What you want to do was better supported in Qpack1, but I had a hard time to find a general way to cover all possible combinations that could be of interest. For Qpack2 you need to do some tricks.) Let us use T as example. If you want to get out the Kb matching T, you can e.g. deactivate all present retrieval variables, and just set T as retrieval variable, and Q.T.L2 = true. Then make a linear inversion and Kb = L2.J. (You can do the same thing for H2O. The uncertainty associated with O2 should be negligible.) With this Kb, the retrieval error due to T is calculated as Sr = G*Kb*St*Kb^T*G^T where St is the temperature covariance matrix. The above should answer your direct question. However, if the error due to T is significant, you can do better. This simple by retrieving T in parallel to O3. The extra calculation cost is quite small, and I would recommend you to do this as the error related to T will then be smaller. If you don't care about the result for T, you just set Q.T.L2=false. If you still want to calculate the error due to T, you can do as above, or use L2.jq and L2.ji to extract the part of L2.J that matches T, as I tried to describe in my last answer. If H2O should also be retrieved depends on your exact set-up. Bye, Patrick On 10/29/15 14:19, Facundo Orte wrote: > Dear Patrick, > Thank you so much for the reply and I am sorry for not be clear. > I set Qpack to retrieve only ozone. Other gases and T (H2O, O2 and T) I > set as Q.ABS_SPECIES.RETRIEVE=false, because I am not interesting in > water vapor, oxygen, and T, but I include NCEP profiles of > these gases and temperature in Q.DEFINITIONS.m for ARTS calculation and > to model the spectrum. O2 is included because the frequency range of > measurement is 110.83GHz and this gas also absorb the signal. These > profiles have some uncertainties and I want to introduce these > uncertainties in the covariance matrix for the cost calculation and so > on. What I need is to include these uncertainties in the covariance matrix. > > I think that I can include it in the covariance matrix doing: > > S^(-1)=Se^(-1) + Kb^T*Sb^(-1)*Kb > > where Kb is the jacobian of non-retrieval parameters (in my case H2O, T, > and O2), Sb is the covariance matrix related with the non-retrieval > parameters and Se is the covariance matrix related with noise. S is the > covariance matrix used to calculate the cost as following. > > cost=[y-F(x,b)]^T * *S*^(-1) * [y-F(x,b)] + [x-xa]^T * Sx^(-1) * [x-xa] > > At this time, I introduce the covariance matrix only related with noise > (S=Se) and I not include the uncertainties for water vapor, T and O2 > profiles. > > I think that the Jacobians of non-retrieval parameters are similar for > different measurements. So, my idea is to calculate these jacobians one > time and use it then for the calculation of different measurements. It > will avoid the calculation of these jacobians in each measurement. > > My problem is that I do not know how to include the uncertainties for > non-retrieval parameters profiles in the covariance matrix S. > > Thanks in advance > Best regards > > 2015-10-27 16:55 GMT-03:00 Patrick Eriksson > mailto:[email protected]>>: > > Dear Facundo, > > As I understand your question, the answer is no. Or at least, this > can not be done automatically. > > However, if you think that H2O and temperature can give substantial > errors (I can not see how O2 should give rise to an error), you > should retrieve H2O and T in parallel to O3. That will decrease the > impact of H2O and T. In short, OEM will then adjust as far as > possible to H2O and T uncertainties. The impact of H2O and T will > then be included in the standard error estimate. > > You have seen that Qpack can give you the error covariance matrix? > > If you retrieve H2O and T, what you call df/dH2O and df/dT are part > of the (total) Jacobian matrix, that you can trigger Qpack to > output, i.e. L2.J. The fields L2.jq and L2.ji give you information > about what part of J that belong to H2O and T etc. That should give > you a way to calculate separate errors. > > Bye, > > Patrick > > > > > On 10/27/15 20:15, Facundo Orte wrote: > > Dear all, > I am very new using Qpack. I am setting Qpack to retrieve ozone > profiles > using ARTS. At this moment, I am able to retrieve coherent ozone > profiles, but to calculate covariance matrix I am trying to get the > jacobian for water vapor (df/dH2O), oxygen(df/dO2) and temperature > (df/dT) (to include the uncertainties of these gases and > temperature), > due to these gases absorb radiation in the frequency range of >
Re: [Qpack] jacobian non-retrieval parameters
Dear Patrick, Thank you so much for the reply and I am sorry for not be clear. I set Qpack to retrieve only ozone. Other gases and T (H2O, O2 and T) I set as Q.ABS_SPECIES.RETRIEVE=false, because I am not interesting in water vapor, oxygen, and T, but I include NCEP profiles of these gases and temperature in Q.DEFINITIONS.m for ARTS calculation and to model the spectrum. O2 is included because the frequency range of measurement is 110.83GHz and this gas also absorb the signal. These profiles have some uncertainties and I want to introduce these uncertainties in the covariance matrix for the cost calculation and so on. What I need is to include these uncertainties in the covariance matrix. I think that I can include it in the covariance matrix doing: S^(-1)=Se^(-1) + Kb^T*Sb^(-1)*Kb where Kb is the jacobian of non-retrieval parameters (in my case H2O, T, and O2), Sb is the covariance matrix related with the non-retrieval parameters and Se is the covariance matrix related with noise. S is the covariance matrix used to calculate the cost as following. cost=[y-F(x,b)]^T * *S*^(-1) * [y-F(x,b)] + [x-xa]^T * Sx^(-1) * [x-xa] At this time, I introduce the covariance matrix only related with noise (S=Se) and I not include the uncertainties for water vapor, T and O2 profiles. I think that the Jacobians of non-retrieval parameters are similar for different measurements. So, my idea is to calculate these jacobians one time and use it then for the calculation of different measurements. It will avoid the calculation of these jacobians in each measurement. My problem is that I do not know how to include the uncertainties for non-retrieval parameters profiles in the covariance matrix S. Thanks in advance Best regards 2015-10-27 16:55 GMT-03:00 Patrick Eriksson : > Dear Facundo, > > As I understand your question, the answer is no. Or at least, this can not > be done automatically. > > However, if you think that H2O and temperature can give substantial errors > (I can not see how O2 should give rise to an error), you should retrieve > H2O and T in parallel to O3. That will decrease the impact of H2O and T. In > short, OEM will then adjust as far as possible to H2O and T uncertainties. > The impact of H2O and T will then be included in the standard error > estimate. > > You have seen that Qpack can give you the error covariance matrix? > > If you retrieve H2O and T, what you call df/dH2O and df/dT are part of the > (total) Jacobian matrix, that you can trigger Qpack to output, i.e. L2.J. > The fields L2.jq and L2.ji give you information about what part of J that > belong to H2O and T etc. That should give you a way to calculate separate > errors. > > Bye, > > Patrick > > > > > On 10/27/15 20:15, Facundo Orte wrote: > >> Dear all, >> I am very new using Qpack. I am setting Qpack to retrieve ozone profiles >> using ARTS. At this moment, I am able to retrieve coherent ozone >> profiles, but to calculate covariance matrix I am trying to get the >> jacobian for water vapor (df/dH2O), oxygen(df/dO2) and temperature >> (df/dT) (to include the uncertainties of these gases and temperature), >> due to these gases absorb radiation in the frequency range of >> measurement. Is it possible to get these jacobians at the same time as >> the retrieval species (O3)? >> Thanks in advance >> >> Regards >> Facundo >> >> >> ___ >> qpack mailing list >> [email protected] >> https://www.sat.ltu.se/mailman/listinfo/qpack >> >> ___ qpack mailing list [email protected] https://www.sat.ltu.se/mailman/listinfo/qpack
Re: [Qpack] jacobian non-retrieval parameters
Dear Facundo, As I understand your question, the answer is no. Or at least, this can not be done automatically. However, if you think that H2O and temperature can give substantial errors (I can not see how O2 should give rise to an error), you should retrieve H2O and T in parallel to O3. That will decrease the impact of H2O and T. In short, OEM will then adjust as far as possible to H2O and T uncertainties. The impact of H2O and T will then be included in the standard error estimate. You have seen that Qpack can give you the error covariance matrix? If you retrieve H2O and T, what you call df/dH2O and df/dT are part of the (total) Jacobian matrix, that you can trigger Qpack to output, i.e. L2.J. The fields L2.jq and L2.ji give you information about what part of J that belong to H2O and T etc. That should give you a way to calculate separate errors. Bye, Patrick On 10/27/15 20:15, Facundo Orte wrote: > Dear all, > I am very new using Qpack. I am setting Qpack to retrieve ozone profiles > using ARTS. At this moment, I am able to retrieve coherent ozone > profiles, but to calculate covariance matrix I am trying to get the > jacobian for water vapor (df/dH2O), oxygen(df/dO2) and temperature > (df/dT) (to include the uncertainties of these gases and temperature), > due to these gases absorb radiation in the frequency range of > measurement. Is it possible to get these jacobians at the same time as > the retrieval species (O3)? > Thanks in advance > > Regards > Facundo > > > ___ > qpack mailing list > [email protected] > https://www.sat.ltu.se/mailman/listinfo/qpack > ___ qpack mailing list [email protected] https://www.sat.ltu.se/mailman/listinfo/qpack
