On the use of multiple criteria distance indexes to find robust cash management policies

[EN] Cash management decision-making can be handled from a multiobjective perspective by optimizing not only cost but also risk. Nevertheless, choosing the best policies under a changing context is by no means straightforward. To this end, we rely on compromise programming to incorporate robustness as an additional goal to cost and risk within a multiobjective framework. As a result, we propose to calculate robustness as a multiple criteria distance index that is able to identify the best compromise policies in terms of cost and risk. Such policies are also robust to cash flow regime changes. We show its utility by transforming the Miller and Orr s cash management model into its robust counterpart using real data from an industrial company.Ministerio de Economia y Competitividad [grant number Collectiveware TIN2015-66863-C2-1-R], [grant number 2014 SGR 118]. Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D. (2019). On the use of multiple criteria distance indexes to find robust cash management policies. INFOR Information Systems and Operational Research. 57(3):345-360. https://doi.org/10.1080/03155986.2017.1282291S34536057

An analytic derivation of the efficient frontier in biobjective cash management and its implications for policies

[EN] Cash managers who optimize returns and risk rely on biobjective optimization models to select the best policies according to their risk preferences. In the related portfolio selection problem, Merton (J Financ Quant Anal 7(4):1851¿1872, 1972) provided the first analytical derivation of the efficient frontier with all non-dominated return and risk combinations. This first proposal was later extended to account for three or more criteria by other authors. However, the cash management literature needs an analytical derivation of the efficient frontier to help cash managers evaluate the implications of selecting policies and risk measures. In this paper, we provide three analytic derivations of the efficient frontier determining a closed-form solution for the expected returns and risk relationship using three different risk measures. We study its main properties and its theoretical implications for policies. Using the variance of returns as a risk measure imposes limitations due to invertibility reasons.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Salas-Molina, F.; Pla Santamaría, D.; Rodriguez-Aguilar, JA. (2023). An analytic derivation of the efficient frontier in biobjective cash management and its implications for policies. Annals of Operations Research (Online). 328(2):1523-1536. https://doi.org/10.1007/s10479-023-05433-z152315363282Baumol, W. J. (1952). The transactions demand for cash: An inventory theoretic approach. The Quarterly Journal of Economics, 66(4), 545–556.Constantinides, G. M., & Richard, S. F. (1978). Existence of optimal simple policies for discounted-cost inventory and cash management in continuous time. Operations Research, 26(4), 620–636.da Costa Moraes, M. B., Nagano, M. S., Sobreiro, V. A., et al. (2015). Stochastic cash flow management models: A literature review since the 1980s. In P. Guarnieri (Ed.), Decision Models in Engineering and Management (pp. 11–28). Berlin: Springer.Markowitz, H. (1952). The Portfolio selection. Journal of Finance, 7(1), 77–91.Merton, R. C. (1972). An analytic derivation of the efficient portfolio frontier. Journal of Financial and Quantitative Analysis, 7(4), 1851–1872.Miller, M. H., & Orr, D. (1966). A model of the demand for money by firms. The Quarterly Journal of Economics, 80(3), 413–435.Qi, Y. (2020). Parametrically computing efficient frontiers of portfolio selection and reporting and utilizing the piecewise-segment structure. Journal of the Operational Research Society, 71(10), 1675–1690.Qi, Y. (2022). Classifying the minimum-variance surface of multiple-objective portfolio selection for capital asset pricing models. Annals of Operations Research, 311(2), 1203–1227.Qi, Y., & Li, X. (2020). On imposing ESG constraints of portfolio selection for sustainable investment and comparing the efficient frontiers in the weight space. SAGE Open, 10(4), 1–17.Qi, Y., & Steuer, R. E. (2020). On the analytical derivation of efficient sets in quad-and-higher criterion portfolio selection. Annals of Operations Research, 293(2), 521–538.Qi, Y., Steuer, R. E., & Wimmer, M. (2017). An analytical derivation of the efficient surface in portfolio selection with three criteria. Annals of Operations Research, 251(1–2), 161–177.Salas-Molina, F. (2019). Selecting the best risk measure in multiobjective cash management. International Transactions in Operational Research, 26(3), 929–945.Salas-Molina, F. (2020). Risk-sensitive control of cash management systems. Operational Research, 20(2), 1159–1176.Salas-Molina, F., Pla-Santamaria, D., & Rodríguez-Aguilar, J. A. (2018). Empowering cash managers through compromise programming. In H. Masri, B. Perez-Gladish, & C. Zopounidis (Eds.), Financial decision aid using multiple criteria (pp. 149–173). Berlin: Springer.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2018). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1), 515–529.Salas-Molina, F., Rodriguez-Aguilar, J. A., & Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research, 261(1), 275–288.Salas-Molina, F., Rodriguez-Aguilar, J. A., Pla-Santamaria, D., & García-Bernabeu, A. (2021). On the formal foundations of cash management systems. Operational Research, 21(2), 1081–1095.Salas-Molina, F., Rodríguez-Aguilar, J. A., & Guillen, M. (2023). A multidimensional review of the cash management problem. Financial Innovation, 9(67), 1–35.Savage, L. J. (1951). The theory of statistical decision. Journal of the American Statistical Association, 46(253), 55–67.Schroeder, P., & Kacem, I. (2019). Optimal cash management with uncertain, interrelated and bounded demands. Computers & Industrial Engineering, 133, 195–206.Schroeder, P., & Kacem, I. (2020). Competitive difference analysis of the cash management problem with uncertain demands. European Journal of Operational Research, 283(3), 1183–1192

Characterizing compromise solutions for investors with uncertain risk preferences

[EN] The optimum portfolio selection for an investor with particular preferences was proven to lie on the normalized efficient frontier between two bounds defined by the Ballestero (1998) bounding theorem. A deeper understanding is possible if the decision-maker is provided with visual and quantitative techniques. Here, we derive useful insights as a way to support investor's decision-making through: (i) a new theorem to assess balance of solutions; (ii) a procedure and a new plot to deal with discrete efficient frontiers and uncertain risk preferences; and (iii) two quality metrics useful to predict long-run performance of investors.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D. (2019). Characterizing compromise solutions for investors with uncertain risk preferences. Operational Research. 19(3):661-677. https://doi.org/10.1007/s12351-017-0309-6S661677193Amiri M, Ekhtiari M, Yazdani M (2011) Nadir compromise programming: a model for optimization of multi-objective portfolio problem. Expert Syst Appl 38(6):7222–7226Ballestero E (1998) Approximating the optimum portfolio for an investor with particular preferences. J Oper Res Soc 49:998–1000Ballestero E (2007) Compromise programming: a utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. Eur J Oper Res 182(3):1369–1382Ballestero E, Pla-Santamaria D (2004) Selecting portfolios for mutual funds. Omega 32(5):385–394Ballestero E, Pla-Santamaria D, Garcia-Bernabeu A, Hilario A (2015) Portfolio selection by compromise programming. In: Ballestero E, Pérez-Gladish B, Garcia-Bernabeu A (eds) Socially responsible investment. A multi-criteria decision making approach, vol 219. Springer, Switzerland, pp 177–196Ballestero E, Romero C (1996) Portfolio selection: a compromise programming solution. J Oper Res Soc 47(11):1377–1386Ballestero E, Romero C (1998) Multiple criteria decision making and its applications to economic problems. Kluwer Academic Publishers, BerlinBilbao-Terol A, Pérez-Gladish B, Arenas-Parra M, Rodríguez-Uría MV (2006) Fuzzy compromise programming for portfolio selection. Appl Math Comput 173(1):251–264Bravo M, Ballestero E, Pla-Santamaria D (2012) Evaluating fund performance by compromise programming with linear-quadratic composite metric: an actual case on the caixabank in spain. J Multi-Criteria Decis Anal 19(5–6):247–255Ehrgott M, Klamroth K, Schwehm C (2004) An MCDM approach to portfolio optimization. Eur J Oper Res 155(3):752–770Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874Hernández-Orallo J, Flach P, Ferri C (2013) ROC curves in cost space. Mach Learn 93(1):71–91Markowitz H (1952) Portfolio selection. J Finance 7(1):77–91Pla-Santamaria D, Bravo M (2013) Portfolio optimization based on downside risk: a mean-semivariance efficient frontier from dow jones blue chips. Ann Oper Res 205(1):189–201Ringuest JL (1992) Multiobjective optimization: behavioral and computational considerations. Springer Science & Business Media, BerlinSteuer RE, Qi Y, Hirschberger M (2007) Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Ann Oper Res 152(1):297–317Xidonas P, Mavrotas G, Krintas T, Psarras J, Zopounidis C (2012) Multicriteria portfolio management. Springer, BerlinYu P-L (1973) A class of solutions for group decision problems. Manag Sci 19(8):936–946Yu P-L (1985) Multiple criteria decision making: concepts, techniques and extensions. Plenum Press, BerlinZeleny M (1982) Multiple criteria decision making. McGraw-Hill, New Yor

Effects of the MY34/2018 Global Dust Storm as Measured by MSL REMS in Gale Crater

The Rover Environmental Monitoring Station (REMS) instrument is on board NASA’s Mars Science Laboratory (MSL) Curiosity rover. REMS has been measuring surface pressure, air, and ground brightness temperature, relative humidity, and ultraviolet (UV) irradiance since MSL’s landing in 2012. In Mars Year (MY) 34 (2018) a global dust storm reached Gale Crater at Ls ~ 190°. REMS offers a unique opportunity to better understand the impact of a global dust storm on local environmental conditions, which complements previous observations by the Viking landers and Mars Exploration Rovers. All atmospheric variables measured by REMS are strongly affected albeit at different times. During the onset phase, the daily maximum UV radiation decreased by 90% between sols 2075 (opacity ~1) and 2085 (opacity ~8.5). The diurnal range in ground and air temperatures decreased by 35 and 56 K, respectively, with also a diurnal-average decrease of ~2 and 4 K respectively. The maximum relative humidity, which occurs right before sunrise, decreased to below 5%, compared with prestorm values of up to 29%, due to the warmer air temperatures at night, while the inferred water vapor abundance suggests an increase during the storm. Between sols 2085 and 2130, the typical nighttime stable inversion layer was absent near the surface as ground temperatures remained warmer than near-surface air temperatures. Finally, the frequency domain behavior of the diurnal pressure cycle shows a strong increase in the strength of the semidiurnal and terdiurnal modes peaking after the local opacity maximum, also suggesting differences in the dust abundance inside and outside Gale.Key PointsAtmospheric opacity over Gale Crater was increased by more than 8 times and disturbed all the atmospheric variables measured by REMSREMS data suggest that the nighttime near-surface atmosphere stability was reduced and its water abundance increased during the GDSThe semidiurnal mode peaked after the local opacity maximum, suggesting different dust abundance inside and outside GalePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151294/1/jgre21177_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151294/2/jgre21177.pd

Meteorological predictions for Mars 2020 Perseverance rover landing site at Jezero crater

Correction to: Space Sci Rev (2020) 216:148 https://doi.org/10.1007/s11214-020-00763-xPeer reviewe

The observed winter circulation at InSight’s landing site and its impact on understanding the year-round circulation and aeolian activity in Elysium Planitia and Gale Crater

The InSight lander landed at 135.62°E, 4.50°N in Elysium Planitia, less than 550km from the landing site of the Mars Science Laboratory Curiosity rover, at 137.44°E, 4.59°S in Gale Crater. Both missions carried a suite of meteorological instruments, but regrettably MSL’s wind sensor was damaged on landing and failed completely after ~two Mars years [1]. The resultant data gaps and biases made it very difficult to properly characterize the diurnal and seasonal cycles of wind speed and direction in Gale Crater, a region of extreme topography containing numerous aeolian features. Fortunately, InSight’s wind sensor does not appear to have been damaged on landing, and is returning excellent wind data for the landing season (Ls~300°, northern winter). This valuable dataset has already allowed us to characterize the Elysium Planitia circulation at one time of year, and - in combination with atmospheric models - provides vital insight into what controls the circulation in this region of Mars. In turn, this better understanding of the regional circulation allows us to better interpret the local scale winds partially measured by MSL

NG2 antigen is involved in leukemia invasiveness and central nervous system infiltration in MLL-rearranged infant B-ALL

Mixed-lineage leukemia (MLL)-rearranged (MLLr) infant B-cell acute lymphoblastic leukemia (iMLLr-B-ALL) has a dismal prognosis and is associated with a pro-B/mixed phenotype, therapy refractoriness and frequent central nervous system (CNS) disease/relapse. Neuron-glial antigen 2 (NG2) is specifically expressed in MLLr leukemias and is used in leukemia immunophenotyping because of its predictive value for MLLr acute leukemias. NG2 is involved in melanoma metastasis and brain development; however, its role in MLL-mediated leukemogenesis remains elusive. Here we evaluated whether NG2 distinguishes leukemia-initiating/propagating cells (L-ICs) and/or CNS-infiltrating cells (CNS-ICs) in iMLLr-B-ALL. Clinical data from the Interfant cohort of iMLLr-B-ALL demonstrated that high NG2 expression associates with lower event-free survival, higher number of circulating blasts and more frequent CNS disease/relapse. Serial xenotransplantation of primary MLL-AF4 + leukemias indicated that NG2 is a malleable marker that does not enrich for L-IC or CNS-IC in iMLLr-B-All. However, NG2 expression was highly upregulated in blasts infiltrating extramedullar hematopoietic sites and CNS, and specific blockage of NG2 resulted in almost complete loss of engraftment. Indeed, gene expression profiling of primary blasts and primografts revealed a migratory signature of NG2 + blasts. This study provides new insights on the biology of NG2 in iMLLr-B-ALL and suggests NG2 as a potential therapeutic target to reduce the risk of CNS disease/relapse and to provide safer CNS-directed therapies for iMLLr-B-ALL

Daratumumab displays in vitro and in vivo anti-tumor activity in models of B-cell non-Hodgkin lymphoma and improves responses to standard chemo-immunotherapy regimens

Altres ajuts: This work was carried out at the Esther Koplowitz Center, Barcelona. Genmab and Janssen pharmaceuticals funded this research. Additional grants that contributed to this work included: [...], and CIBERONC (CB16/12/00334 and CB16/12/00225).CD38 is expressed in several types of non-Hodgkin lymphoma (NHL) and constitutes a promising target for antibody-based therapy. Daratumumab (Darzalex) is a first-in-class anti-CD38 antibody approved for the treatment of relapsed/refractory (R/R) multiple myeloma (MM). It has also demonstrated clinical activity in Waldenström macroglobulinaemia and amyloidosis. Here, we have evaluated the activity and mechanism of action of daratumumab in preclinical in vitro and in vivo models of mantle cell lymphoma (MCL), follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL), as monotherapy or in combination with standard chemo-immunotherapy. In vitro, daratumumab engages Fc-mediated cytotoxicity by antibody-dependent cell cytotoxicity and antibody-dependent cell phagocytosis in all lymphoma subtypes. In the presence of human serum, complement-dependent cell cytotoxicity was marginally engaged. We demonstrated by Selective Plane Illumination Microscopy that daratumumab fully penetrated a three-dimensional (3D) lymphoma organoid and decreased organoid volume. In vivo, daratumumab completely prevents tumor outgrowth in models of MCL and FL, and shows comparable activity to rituximab in a disseminated in vivo model of blastic MCL. Moreover, daratumumab improves overall survival (OS) in a mouse model of transformed CD20 FL, where rituximab showed limited activity. Daratumumab potentiates the antitumor activity of CHOP and R-CHOP in MCL and FL xenografts. Furthermore, in a patient-derived DLBCL xenograft model, daratumumab anti-tumor activity was comparable to R-CHOP and the addition of daratumumab to either CHOP or R-CHOP led to full tumor regression. In summary, daratumumab constitutes a novel therapeutic opportunity in certain scenarios and these results warrant further clinical development

Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain

his paper proposes a compromise programming (CP) model to help investors decide whether to construct photovoltaic power plants with government financial support. For this purpose, we simulate an agreement between the government, who pursues political prices (guaranteed prices) as low as possible, and the project sponsor who wants returns (stochastic cash flows) as high as possible. The sponsor s decision depends on the positive or negative result of this simulation, the resulting simulated price being compared to the effective guaranteed price established by the country legislation for photovoltaic energy. To undertake the simulation, the CP model articulates variables such as ranges of guaranteed prices, tech- nical characteristics of the plant, expected energy to be generated over the investment life, investment cost, cash flow probabilities, and others. To determine the CP metric, risk aver- sion is assumed. As an actual application, a case study on photovoltaic power investment in Extremadura, western Spain, is developed in detail.Garcia-Bernabeu, A.; Benito Benito, A.; Bravo Selles, M.; Pla Santamaría, D. (2015). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain. Annals of Operations Research. 1-12. doi:10.1007/s10479-015-1836-2S112Andrews, R. W., Pollard, A., & Pearce, J. M. (2012). Improved parametric empirical determination of module short circuit current for modelling and optimization of solar photovoltaic systems. Solar Energy, 86(9), 2240–2254.Anwar, Y., & Mulyadi, M. S. (2011). Income tax incentives on renewable energy industry: Case of geothermal industry in USA and Indonesia. African Journal of Business Management, 5(31), 12264–12270.Aouni, B., & Kettani, O. (2001). Goal programming model: A glorious history and a promising future. European Journal of Operational Research, 133(2), 225–231.Ballestero, E. (1997). Selecting the CP metric: A risk aversion approach. European Journal of Operational Research, 97(3), 593–596.Ballestero, E. (2000). Project finance: A multicriteria approach to arbitration. Journal of Operational Research Society, 51, 183–197.Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., Pérez-Gladish, B., Arenas-Parra, M., & BilbaoTerol, A. (2009). Selecting portfolios given multiple Eurostoxx-based uncertainty scenarios: A stochastic goal programming approach from fuzzy betas. INFOR: Information Systems and Operational Research, 47(1), 59–70.Ballestero, E., & Plà-Santamaría, D. (2003). Portfolio selection on the Madrid exchange: A compromise programming model. International Transactions in Operational Research, 10(1), 33–51.Ballestero, E., & Pla-Santamaria, D. (2004). Selecting portfolios for mutual funds. Omega, 32(5), 385–394.Ballestero, E., & Pla-Santamaria, D. (2005). Grading the performance of market indicators with utility benchmarks selected from Footsie: A 2000 case study. Applied Economics, 37(18), 2147–2160.Ballestero, E., & Romero, C. (1996). Portfolio selection: A compromise programming solution. Journal of the Operational Research Society, 47, 1377–1386.Bastian-Pinto, C., Brandão, L., & de Lemos Alves, M. (2010). Valuing the switching flexibility of the ethanol–gas flex fuel car. Annals of Operations Research, 176(1), 333–348.Branker, K., Pathak, M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews, 15(9), 4470–4482.Casares, F., Lopez-Luque, R., Posadillo, R., & Varo-Martinez, M. (2014). Mathematical approach to the characterization of daily energy balance in autonomous photovoltaic solar systems. Energy, 72, 393–404.Chatterji, A. K., Levine, D. I., & Toffel, M. W. (2009). How well do social ratings actually measure corporate social responsibility? Journal of Economics & Management Strategy, 18(1), 125–169.Copeland, T. E., & Weston, J. (1988). Financial theory and corporate policy. Reading, Massachusetts: Addison-Wesley.Gallagher, K. S. (2013). Why & how governments support renewable energy. Daedalus, 142(1), 59–77.García-Cascales, M. S., Lamata, M. T., & Sánchez-Lozano, J. M. (2012). Evaluation of photovoltaic cells in a multi-criteria decision making process. Annals of Operations Research, 199(1), 373–391.Gupta, S. (2012). Financing renewable energy. In F. L. Toth (Ed.), Energy for development (pp. 171–186). Springer.Karaarslan, A. (2012). Obtaining renewable energy from piezoelectric ceramics using Sheppard–Taylor converter. International Review of Electrical Engineering, 7(2), 3949–3956.Koellner, T., Weber, O., Fenchel, M., & Scholz, R. (2005). Principles for sustainability rating of investment funds. Business Strategy and the Environment, 14(1), 54–70.Lorenzo, E., & Navarte, L. (2000). On the usefulness of stand-alone PV sizing methods. Progress in Photovoltaics: Research and Applications, 8(4), 391–409.Lüdeke-Freund, F., & Loock, M. (2011). Debt for brands: Tracking down a bias in financing photovoltaic projects in Germany. Journal of Cleaner Production, 19(12), 1356–1364.Mavrotas, G., Diakoulaki, D., & Capros, P. (2003). Combined MCDA-IP approach for project selection in the electricity market. Annals of Operations Research, 120(1–4), 159–170.Mendez-Rodriguez, P., Garcia Bernabeu, A., Hilario, A., & Perez-Gladish, B. (2013). Some effects on the efficient frontier of the investment strategy: A preliminary approach. Recta, 14, 131–144.Michelson, G., Wailes, N., Van Der Laan, S., & Frost, G. (2004). Ethical investment processes and outcomes. Journal of Business Ethics, 52(1), 1–10.Mills, S. J. (1994). Project finance for renewable energy. Renewable energy, 5(1–4), 700–708.ORourke, A. (2003). The message and methods of ethical investment. Journal of Cleaner Production, 11(6), 683–693.Pla-Santamaria, D., & Bravo, M. (2013). Portfolio optimization based on downside risk: A mean-semivariance efficient frontier from Dow Jones blue chips. Annals of Operations Research, 205(1), 189–201.Richter, N. (2009). Renewable project finance options: ITC, PTC, or cash grant? Power, 153(5), 90–92.Schrader, U. (2006). Ignorant advice-customer advisory service for ethical investment funds. Business Strategy and the Environment, 15(3), 200–214.Sitarz, S. (2013). Compromise programming with tehebycheff norm for discrete stochastic orders. Annals of Operations Research, 211(1), 433–446.van de Kaa, G., Rezaei, J., Kamp, L., & de Winter, A. (2014). Photovoltaic technology selection: A fuzzy MCDM approach. Renewable and Sustainable Energy Reviews, 32, 662–670.Yaqub, M., Shahram Sarkni, P., & Mazzuchi, T. (2012). Feasibility analysis of solar photovoltaic commercial power generation in California. Engineering Management Journal, 24(4), 36–49.Yazdani-Chamzini, A., Fouladgar, M. M., Zavadskas, E. K., & Moini, S. H. H. (2013). Selecting the optimal renewable energy using multi criteria decision making. Journal of Business Economics and Management, 14(5), 957–978.Yu, P. (1985). Multiple criteria decision making: Concepts, techniques and extensions. New York: Springer.Zeleny, M. (1982). Multiple criteria decision making (Vol. 25). New York: McGraw-Hill.Zhao, R., Shi, G., Chen, H., Ren, A., & Finlow, D. (2011). Present status and prospects of photovoltaic market in China. Energy Policy, 39(4), 2204–2207

First Atmospheric Results from InSight APSS

NASA’s Mars InSight Spacecraft landed on Nov 26, 2018 (Ls=295°) in Elysium Planitia (~4.5°N, 136°E). InSight’s main scientific purpose is to investigate the interior structure and heat flux from Mars, but it is also equipped with instrumentation that can serve as a very capable meteorological station. To remove unwanted environmental noise from the seis- mic signals, InSight carries a very precise pressure sensor (PS) and the first magnetometer (IFG) to the surface of Mars. Additionally, to aid in removing the atmospheric pressure-induced seismic noise, and to identify periods when wind-induced seismic noise may reduce sensitivity, InSight also carries a pair of Wind and Air temperature sensors (TWINS). These three sensors comprise the Auxiliary Payload Sensor Suite (APSS) [1]. Complementing this are a radiometer in the HP3 suite to measure surface radiance, the seismic measurements of SEIS which can record interesting atmospheric phenomena, and the InSight cameras to image clouds and dust devils and estimate atmospheric opacity from dust or clouds. The Lander also carried accelerometers that can be used to reconstruct the at- mospheric structure during descent. We will discuss results drawn from atmospheric measurements on board InSight from the first months of operation, high- lighting the new perspectives permitted by the novel high-frequency, and continuous nature of the InSight data acquisition. Details on pre-landing scientific perspectives for atmospheric science with InSight are found in [2]